MLG Publication List

All publications

An overview from Di-fusion. Find our current PHD theses subjects here.

2018

Carcillo, Fabrizio

Beyond Supervised Learning in Credit Card Fraud Detection: A Dive into Semi-supervised and Distributed Learning PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/272119b,
title = {Beyond Supervised Learning in Credit Card Fraud Detection: A Dive into Semi-supervised and Distributed Learning},
author = {Fabrizio Carcillo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/272119/5/ContratDiCarcillo.pdf},
year = {2018},
date = {2018-01-01},
abstract = {The expansion of the electronic commerce, as well as the increasing confidence of customers in electronic payments, makes of fraud detection a critical issue. The design of a prompt and accurate Fraud Detection System is a priority for many organizations in the business of credit cards. In this thesis we present a series of studies to increase the precision and the speed of fraud detection system. The thesis has three main contributions. The first concerns the integration of unsupervised techniques and supervised classifiers. We proposed several approaches to integrate outlier scores in the detection process and we found that the accuracy of a conventional classifier may be improved when information about the input distribution is used to augment the training set.The second contribution concerns the role of active learning in Fraud Detection. We have extensively compared several state-of-the-art techniques and found that Stochastic Semi-supervised Learning is a convenient approach to tackle the Selection Bias problem in the active learning process.The third contribution of the thesis is the design, implementation and assessment of SCARFF, an original framework for near real-time Streaming Fraud Detection. This framework integrates Big Data technology (notably tools like Kafka, Spark and Cassandra) with a machine learning approach to deal with imbalance, non-stationarity and feedback latency in a scalable manner. Experimental results on a massive dataset of real credit card transactions have showed that our framework is scalable, efficient and accurate over a big stream of transactions.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

`e, Nathaniel Mon P; Lenaerts, Tom; Pacheco, Jorge M J M; Dingli, David

Evolutionary Dynamics of Paroxysmal Nocturnal Hemoglobinuria Journal Article

In: PLoS computational biology, 2018, (Language of publication: en).

Links | BibTeX

Bizet, Martin

Bioinformatic inference of a prognostic epigenetic signature of immunity in breast cancers PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/265092b,
title = {Bioinformatic inference of a prognostic epigenetic signature of immunity in breast cancers},
author = {Martin Bizet},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/265092/7/ContratDiBizet.pdf},
year = {2018},
date = {2018-01-01},
abstract = {L’altération des marques épigénétiques est de plus en plus reconnue comme une caractéristique fondamentale des cancers. Dans cette th`ese, nous avons utilisé des profils de méthylation de l’ADN en vue d’améliorer la classification des patients atteints du cancer du sein gr^ace `a une approche basée sur l’apprentissage automatique. L’objectif `a long terme est le développement d’outils cliniques de médecine personnalisée. Les données de méthylation de l’ADN furent acquises `a l’aide d’une puce `a ADN dédiée `a la méthylation, appelée Infinium. Cette technologie est récente comparée, par exemple, aux puces d’expression génique et son prétraitement n’est pas encore standardisé. La premi`ere partie de cette th`ese fut donc consacrée `a l’évaluation des méthodes de normalisation par comparaison des données normalisées avec d’autres technologies (pyroséquenccage et RRBS) pour les deux technologies Infinium les plus récentes (450k et 850k). Nous avons également évalué la couverture de régions biologiquement relevantes (promoteurs et amplificateurs) par les deux technologies. Ensuite, nous avons utilisé les données Infinium (correctement prétraitées) pour développer un score, appelé MeTIL score, qui présente une valeur pronostique et prédictive dans les cancers du sein. Nous avons profité de la capacité de la méthylation de l’ADN `a refléter la composition cellulaire pour extraire une signature de méthylation (c’est-`a-dire un ensemble de positions de l’ADN o`u la méthylation varie) qui refl`ete la présence de lymphocytes dans l’échantillon tumoral. Apr`es une sélection de sites présentant une méthylation spécifique aux lymphocytes, nous avons développé une approche basée sur l’apprentissage automatique pour obtenir une signature d’une tailleoptimale réduite `a cinq sites permettant potentiellement une utilisation en clinique. Apr`es conversion de cette signature en un score, nous avons montré sa spécificité pour les lymphocytes `a l’aide de données externes et de simulations informatiques. Puis, nous avons montré la capacité du MeTIL score `a prédire la réponse `a la chimiothérapie ainsi que son pouvoir pronostique dans des cohortes indépendantes de cancer du sein et, m^eme, dans d’autres cancers.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

L’altération des marques épigénétiques est de plus en plus reconnue comme une caractéristique fondamentale des cancers. Dans cette th`ese, nous avons utilisé des profils de méthylation de l’ADN en vue d’améliorer la classification des patients atteints du cancer du sein gr^ace `a une approche basée sur l’apprentissage automatique. L’objectif `a long terme est le développement d’outils cliniques de médecine personnalisée. Les données de méthylation de l’ADN furent acquises `a l’aide d’une puce `a ADN dédiée `a la méthylation, appelée Infinium. Cette technologie est récente comparée, par exemple, aux puces d’expression génique et son prétraitement n’est pas encore standardisé. La premi`ere partie de cette th`ese fut donc consacrée `a l’évaluation des méthodes de normalisation par comparaison des données normalisées avec d’autres technologies (pyroséquenccage et RRBS) pour les deux technologies Infinium les plus récentes (450k et 850k). Nous avons également évalué la couverture de régions biologiquement relevantes (promoteurs et amplificateurs) par les deux technologies. Ensuite, nous avons utilisé les données Infinium (correctement prétraitées) pour développer un score, appelé MeTIL score, qui présente une valeur pronostique et prédictive dans les cancers du sein. Nous avons profité de la capacité de la méthylation de l’ADN `a refléter la composition cellulaire pour extraire une signature de méthylation (c’est-`a-dire un ensemble de positions de l’ADN o`u la méthylation varie) qui refl`ete la présence de lymphocytes dans l’échantillon tumoral. Apr`es une sélection de sites présentant une méthylation spécifique aux lymphocytes, nous avons développé une approche basée sur l’apprentissage automatique pour obtenir une signature d’une tailleoptimale réduite `a cinq sites permettant potentiellement une utilisation en clinique. Apr`es conversion de cette signature en un score, nous avons montré sa spécificité pour les lymphocytes `a l’aide de données externes et de simulations informatiques. Puis, nous avons montré la capacité du MeTIL score `a prédire la réponse `a la chimiothérapie ainsi que son pouvoir pronostique dans des cohortes indépendantes de cancer du sein et, m^eme, dans d’autres cancers.

Raimondi, Daniele; Orlando, Gabriele; Tabaro, Francesco; Lenaerts, Tom; Rooman, Marianne; Moreau, Yves; Vranken, Wim F

Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome Journal Article

In: Scientific reports, 2018, (Language of publication: en).

Links | BibTeX

Skiba, Grażyna; Starzec, Mateusz; Byrski, Aleksander; Rycerz, Katarzyna; Kisiel-Dorohinicki, Marek; Turek, Wojciech; Krzywicki, Daniel; Lenaerts, Tom; Burguillo, Juan Carlos

Flexible asynchronous simulation of iterated prisoner's dilemma based on actor model Journal Article

In: Simulation modelling practice and theory, 83 , pp. 75-92, 2018, (DOI: 10.1016/j.simpat.2017.12.010).

Abstract | Links | BibTeX

Kieken, Fabien; Loth, Karine; van Nuland, Nico N A J; Tompa, Peter; Lenaerts, Tom

Chemical shift assignments of the partially deuterated Fyn SH2–SH3 domain Journal Article

In: Biomolecular N M R Assignments, 12 (1), pp. 117-122, 2018, (DOI: 10.1007/s12104-017-9792-1).

Abstract | Links | BibTeX

Byrski, Aleksander; Świderska, Ewelina; Łasisz, Jakub; Kisiel-Dorohinicki, Marek; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin

Emergence of population structure in socio-cognitively inspired ant colony optimization Journal Article

In: Computer Science, 19 (1), pp. 81-98, 2018, (DOI: 10.7494/csci.2018.19.1.2594).

Abstract | Links | BibTeX

de Bony, Eric James; Bizet, Martin; Grembergen, Olivier Van; Hassabi, Bouchra; Calonne, Emilie; Putmans, Pascale; Bontempi, Gianluca; cc, Fran

Comprehensive identification of long noncoding RNAs in colorectal cancer Journal Article

In: Oncotarget, 9 (45), pp. 27605-27629, 2018, (DOI: 10.18632/oncotarget.25218).

Abstract | Links | BibTeX

@article{info:hdl:2013/278063,
title = {Comprehensive identification of long noncoding RNAs in colorectal cancer},
author = {Eric James de Bony and Martin Bizet and Olivier Van Grembergen and Bouchra Hassabi and Emilie Calonne and Pascale Putmans and Gianluca Bontempi and Fran{cc}ois Fuks},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/278063},
year = {2018},
date = {2018-01-01},
journal = {Oncotarget},
volume = {9},
number = {45},
pages = {27605-27629},
abstract = {Colorectal cancer (CRC) is one of the most common cancers in humans and a leading cause of cancer-related deaths worldwide. As in the case of other cancers, CRC heterogeneity leads to a wide range of clinical outcomes and complicates therapy. Over the years, multiple factors have emerged as markers of CRC heterogeneity, improving tumor classification and selection of therapeutic strategies. Understanding the molecular mechanisms underlying this heterogeneity remains a major challenge. A considerable research effort is therefore devoted to identifying additional features of colorectal tumors, in order to better understand CRC etiology and to multiply therapeutic avenues. Recently, long noncoding RNAs (lncRNAs) have emerged as important players in physiological and pathological processes, including CRC. Here we looked for lncRNAs that might contribute to the various colorectal tumor phenotypes. We thus monitored the expression of 4898 lncRNA genes across 566 CRC samples and identified 282 lncRNAs reflecting CRC heterogeneity. We then inferred potential functions of these lncRNAs. Our results highlight lncRNAs that may participate in the major processes altered in distinct CRC cases, such as WNT/β-catenin and TGF-β signaling, immunity, the epithelial-to-mesenchymal transition (EMT), and angiogenesis. For several candidates, we provide experimental evidence supporting our functional predictions that they may be involved in the cell cycle or the EMT. Overall, our work identifies lncRNAs associated with key CRC characteristics and provides insights into their respective functions. Our findings constitute a further step towards understanding the contribution of lncRNAs to CRC heterogeneity. They may open new therapeutic opportunities.},
note = {DOI: 10.18632/oncotarget.25218},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Ioannidis, J P A; Bhattacharya, S; Evers, J L H; Veen, Der F V; Somigliana, E; Barratt, C L R; Bontempi, Gianluca; Baird, D T; Crosignani, P; Devroey, P; Diedrich, Klaus; Farquharson, R G; Fraser, L R; Geraedts, Joep Pm M; Gianaroli, Luca; Vecchia, La C; Magli, C; Negri, E; Sunde, A; Tapanainen, J S; Tarlatzis, Basil; Steirteghem, A V; Veiga, A

Protect us from poor-quality medical research Journal Article

In: Human reproduction, 33 (5), pp. 770-776, 2018, (DOI: 10.1093/humrep/dey056).

Abstract | Links | BibTeX

Trepo, Eric; Goossens, Nicolas; Fujiwara, Naoto; Song, Won-Min; Colaprico, Antonio; Marot, Astrid; Spahr, Laurent; Demetter, Pieter; Sempoux, Christine; Im, Gene Y; Saldarriaga, Joan; Gustot, Thierry; `e, Jacques Devi; Thung, Swan SN; Minsart, Charlotte; Serste, Thomas; Bontempi, Gianluca; Abdelrahman, Karim; Henrion, Jean; Degré, Delphine; Lucidi, Valerio; Rubbia-Brandt, Laura; Nair, Venugopalan D; Moreno, Christophe; Deltenre, Pierre; Hoshida, Yujin; Franchimont, Denis

Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis Journal Article

In: Gastroenterology, 154 (4), pp. 965-975, 2018, (DOI: 10.1053/j.gastro.2017.10.048).

Abstract | Links | BibTeX

@article{info:hdl:2013/269084,
title = {Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis},
author = {Eric Trepo and Nicolas Goossens and Naoto Fujiwara and Won-Min Song and Antonio Colaprico and Astrid Marot and Laurent Spahr and Pieter Demetter and Christine Sempoux and Gene Y Im and Joan Saldarriaga and Thierry Gustot and Jacques Devi{`e}re and Swan SN Thung and Charlotte Minsart and Thomas Serste and Gianluca Bontempi and Karim Abdelrahman and Jean Henrion and Delphine Degré and Valerio Lucidi and Laura Rubbia-Brandt and Venugopalan D Nair and Christophe Moreno and Pierre Deltenre and Yujin Hoshida and Denis Franchimont},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/269084},
year = {2018},
date = {2018-01-01},
journal = {Gastroenterology},
volume = {154},
number = {4},
pages = {965-975},
abstract = {Background & Aims: Patients with severe alcoholic hepatitis (AH) have a high risk of death within 90 days. Corticosteroids, which can cause severe adverse events, are the only treatment that increases short-term survival. It is a challenge to predict outcomes of patients with severe AH. Therefore, we developed a scoring system to predict patient survival, integrating baseline molecular and clinical variables. Methods: We obtained fixed liver biopsy samples from 71 consecutive patients diagnosed with severe AH and treated with corticosteroids from July 2006 through December 2013 in Brussels, Belgium (derivation cohort). Gene expression patterns were analyzed by microarrays and clinical data were collected for 180 days. We identified gene expression signatures and clinical data that are associated with survival without liver transplantation at 90 and 180 days after initiation of corticosteroid therapy. Findings were validated using liver biopsies from 48 consecutive patients with severe AH treated with corticosteroids, collected from March 2010 through February 2015 at hospitals in Belgium and Switzerland (validation cohort 1) and in liver biopsies from 20 patients (9 received corticosteroid treatment), collected from January 2012 through May 2015 in the United States (validation cohort 2). Results: We integrated data on expression patterns of 123 genes and the model for end-stage liver disease (MELD) scores to assign patients to groups with poor survival (29% survived 90 days and 26% survived 180 days) and good survival (76% survived 90 days and 65% survived 180 days) (P <.001) in the derivation cohort. We named this assignment system the gene signature–MELD (gs-MELD) score. In validation cohort 1, the gs-MELD score discriminated patients with poor survival (43% survived 90 days) from those with good survival (96% survived 90 days) (P <.001). The gs-MELD score also discriminated between patients with a poor survival at 180 days (34% survived) and a good survival at 180 days (84% survived) (P <.001). The time-dependent area under the receiver operator characteristic curve for the score was 0.86 (95% confidence interval 0.73–0.99) for survival at 90 days, and 0.83 (95% confidence interval 0.71–0.96) for survival at 180 days. This score outperformed other clinical models to predict survival of patients with severe AH in validation cohort 1. In validation cohort 2, the gs-MELD discriminated patients with a poor survival at 90 days (12% survived) from those with a good survival at 90 days (100%) (P <.001). Conclusions: We integrated data on baseline liver gene expression pattern and the MELD score to create the gs-MELD scoring system, which identifies patients with severe AH, treated or not with corticosteroids, most and least likely to survive for 90 and 180 days.},
note = {DOI: 10.1053/j.gastro.2017.10.048},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Background & Aims: Patients with severe alcoholic hepatitis (AH) have a high risk of death within 90 days. Corticosteroids, which can cause severe adverse events, are the only treatment that increases short-term survival. It is a challenge to predict outcomes of patients with severe AH. Therefore, we developed a scoring system to predict patient survival, integrating baseline molecular and clinical variables. Methods: We obtained fixed liver biopsy samples from 71 consecutive patients diagnosed with severe AH and treated with corticosteroids from July 2006 through December 2013 in Brussels, Belgium (derivation cohort). Gene expression patterns were analyzed by microarrays and clinical data were collected for 180 days. We identified gene expression signatures and clinical data that are associated with survival without liver transplantation at 90 and 180 days after initiation of corticosteroid therapy. Findings were validated using liver biopsies from 48 consecutive patients with severe AH treated with corticosteroids, collected from March 2010 through February 2015 at hospitals in Belgium and Switzerland (validation cohort 1) and in liver biopsies from 20 patients (9 received corticosteroid treatment), collected from January 2012 through May 2015 in the United States (validation cohort 2). Results: We integrated data on expression patterns of 123 genes and the model for end-stage liver disease (MELD) scores to assign patients to groups with poor survival (29% survived 90 days and 26% survived 180 days) and good survival (76% survived 90 days and 65% survived 180 days) (P <.001) in the derivation cohort. We named this assignment system the gene signature–MELD (gs-MELD) score. In validation cohort 1, the gs-MELD score discriminated patients with poor survival (43% survived 90 days) from those with good survival (96% survived 90 days) (P <.001). The gs-MELD score also discriminated between patients with a poor survival at 180 days (34% survived) and a good survival at 180 days (84% survived) (P <.001). The time-dependent area under the receiver operator characteristic curve for the score was 0.86 (95% confidence interval 0.73–0.99) for survival at 90 days, and 0.83 (95% confidence interval 0.71–0.96) for survival at 180 days. This score outperformed other clinical models to predict survival of patients with severe AH in validation cohort 1. In validation cohort 2, the gs-MELD discriminated patients with a poor survival at 90 days (12% survived) from those with a good survival at 90 days (100%) (P <.001). Conclusions: We integrated data on baseline liver gene expression pattern and the MELD score to create the gs-MELD scoring system, which identifies patients with severe AH, treated or not with corticosteroids, most and least likely to survive for 90 and 180 days.

Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Bontempi, Gianluca; Mauri, Giancarlo; Castiglioni, Isabella

In-silico integration approach to identify a key miRNA regulating a gene network in aggressive prostate cancer Journal Article

In: International journal of molecular sciences, 19 (3), 2018, (DOI: 10.3390/ijms19030910).

Abstract | Links | BibTeX

@article{info:hdl:2013/270452,
title = {In-silico integration approach to identify a key miRNA regulating a gene network in aggressive prostate cancer},
author = {Claudia Cava and Gloria Bertoli and Antonio Colaprico and Gianluca Bontempi and Giancarlo Mauri and Isabella Castiglioni},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/270452},
year = {2018},
date = {2018-01-01},
journal = {International journal of molecular sciences},
volume = {19},
number = {3},
abstract = {Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC.},
note = {DOI: 10.3390/ijms19030910},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis Journal Article

In: BMC genomics, 19 (1), 2018, (DOI: 10.1186/s12864-017-4423-x).

Abstract | Links | BibTeX

@article{info:hdl:2013/268430,
title = {Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis},
author = {Claudia Cava and Gloria Bertoli and Antonio Colaprico and Catharina Olsen and Gianluca Bontempi and Isabella Castiglioni},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/268430},
year = {2018},
date = {2018-01-01},
journal = {BMC genomics},
volume = {19},
number = {1},
abstract = {Background: Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. Results: We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Conclusions: Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.},
note = {DOI: 10.1186/s12864-017-4423-x},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Reggiani, Claudio; "e, Yann-A; Bontempi, Gianluca

Feature selection in high-dimensional dataset using MapReduce Journal Article

In: Communications in computer and information science, 823 , pp. 101-115, 2018, (DOI: 10.1007/978-3-319-76892-2_8).

Abstract | Links | BibTeX

Gazzo, Andrea

Beyond monogenic diseases: a first collection and analysis of digenic diseases PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/272617b,
title = {Beyond monogenic diseases: a first collection and analysis of digenic diseases},
author = {Andrea Gazzo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/272617/5/ContratDiGazzo.pdf},
year = {2018},
date = {2018-01-01},
abstract = {In the next generation sequencing era many bioinformatics tools have been developed for assisting scientists in their studies on the molecular basis of genetic diseases, often with the aim of identifying the pathogenic variants. As a consequence, in the last decades more than one hundred new disease-gene associations have been discovered. Nevertheless, the genetic basis of many genetic diseases yet remains undisclosed. It has been shown that many diseases considered as monogenic with an imperfect genotype-phenotype correlation or incomplete penetrance are, on the contrary, caused or modulated by more than one mutated gene, meaning that they are in fact oligogenic. Current bioinformatics methods used for identifying pathogenic variants are trained and fine-tuned for identifying a single variant responsible of a disease. This monogenic-oriented approach cannot be used to explore the impact of combinations of variants in different genes on the complexity and genetic heterogeneity of rare diseases. Digenic diseases are the simplest form of oligogenic disease and thus they can provide a conceptual bridge between monogenic and the poorly understood polygenic diseases.The ambition of this thesis is to collect and analyse digenic data, introducing this topic in the bioinformatics field where digenic diseases are still an unexplored branch. This can be divided in two steps: the first consists in the creation of a central repository containing detailed information on digenic diseases; the second is an analysis of their peculiarities, using machine learning methods for studying subclasses of digenic effects.In the first step we developed DIDA (DIgenic diseases DAtabase), a novel database that provides for the first time a curated collection of genes and associated variants involved in digenic diseases. Detailed information related to the digenic mechanism have been manually mined from the medical literature. All instances in DIDA were also assigned to two sub classes of digenic effects, annotated as true digenic (both genes are required for developing the disease) and composite classes (one gene is sufficient to produce the disease phenotype, the second one alters it or change significantly the age of onset).In the second step, we hypothesized that the digenic effect may be related to some biological properties characterizing digenic combinations. Using machine learning methods, we show that a set of variant, gene and higher-level features can differentiate between the true digenic and composite classes with high accuracy. Moreover, we show that a digenic effect decision profile, extracted from the predictive model, motivates why an instance is assigned to either of the two classes.Together, our results show that digenic disease data generates novel insights, providing a glimpse into the oligogenic realm.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

In the next generation sequencing era many bioinformatics tools have been developed for assisting scientists in their studies on the molecular basis of genetic diseases, often with the aim of identifying the pathogenic variants. As a consequence, in the last decades more than one hundred new disease-gene associations have been discovered. Nevertheless, the genetic basis of many genetic diseases yet remains undisclosed. It has been shown that many diseases considered as monogenic with an imperfect genotype-phenotype correlation or incomplete penetrance are, on the contrary, caused or modulated by more than one mutated gene, meaning that they are in fact oligogenic. Current bioinformatics methods used for identifying pathogenic variants are trained and fine-tuned for identifying a single variant responsible of a disease. This monogenic-oriented approach cannot be used to explore the impact of combinations of variants in different genes on the complexity and genetic heterogeneity of rare diseases. Digenic diseases are the simplest form of oligogenic disease and thus they can provide a conceptual bridge between monogenic and the poorly understood polygenic diseases.The ambition of this thesis is to collect and analyse digenic data, introducing this topic in the bioinformatics field where digenic diseases are still an unexplored branch. This can be divided in two steps: the first consists in the creation of a central repository containing detailed information on digenic diseases; the second is an analysis of their peculiarities, using machine learning methods for studying subclasses of digenic effects.In the first step we developed DIDA (DIgenic diseases DAtabase), a novel database that provides for the first time a curated collection of genes and associated variants involved in digenic diseases. Detailed information related to the digenic mechanism have been manually mined from the medical literature. All instances in DIDA were also assigned to two sub classes of digenic effects, annotated as true digenic (both genes are required for developing the disease) and composite classes (one gene is sufficient to produce the disease phenotype, the second one alters it or change significantly the age of onset).In the second step, we hypothesized that the digenic effect may be related to some biological properties characterizing digenic combinations. Using machine learning methods, we show that a set of variant, gene and higher-level features can differentiate between the true digenic and composite classes with high accuracy. Moreover, we show that a digenic effect decision profile, extracted from the predictive model, motivates why an instance is assigned to either of the two classes.Together, our results show that digenic disease data generates novel insights, providing a glimpse into the oligogenic realm.

Reggiani, Claudio

Bioinformatic discovery of novel exons expressed in human brain and their association with neurodevelopmental disorders PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/270994b,
title = {Bioinformatic discovery of novel exons expressed in human brain and their association with neurodevelopmental disorders},
author = {Claudio Reggiani},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/270994/5/ContratDiReggiani.pdf},
year = {2018},
date = {2018-01-01},
abstract = {An important quest in genomics since the publication of the first complete human genome in 2003 has been its functional annotation. DNA holds the instructions to the production of the components necessary for the life of cells and organisms. A complete functional catalog of genomic regions will help the understanding of the cell body and its dynamics, thus creating links between genotype and phenotypic traits. The need for annotations prompted the development of several bioinformatic methods. In the context of promoter and first exon predictors, the majority of models relies principally on structural and chemical properties of the DNA sequence. Some of them integrate information from epigenomic and transcriptomic data as secondary features. Current genomic research asserts that reference genome annotations are far from being fully annotated (human organism included).Physicians rely on reference genome annotations and functional databases to understand disorders with genetic basis, and missing annotations may lead to unresolved cases. Because of their complexity, neurodevelopmental disorders are under study to figure out all genetic regions that are involved. Besides functional validation on model organisms, the search for genotype-phenotype association is supported by statistical analysis, which is typically biased towards known functional regions.This thesis addresses the use of an in-silico integrative analysis to improve reference genome annotations and discover novel functional regions associated with neurodevelopemental disorders. The contributions outlined in this document have practical applications in clinical settings. The presented bioinformatic method is based on epigenomic and transcriptomic data, thus excluding features from DNA sequence. Such integrative approach applied on brain data allowed the discovery of two novel promoters and coding first exons in the human DLG2 gene, which were also found to be statistically associated with neurodevelopmental disorders and intellectual disability in particular. The application of the same methodology to the whole genome resulted in the discovery of other novel exons expressed in brain. Concerning the in-silico method itself, the research demanded a high number of functional and clinical datasets to properly support and validate our discoveries.This work describes a bioinformatic method for genome annotation, in the specific area of promoter and first exons. So far the method has been applied on brain data, and the extension to the whole body data would be a logical by-product. We will leverage distributed frameworks to tackle the even higher amount of data to analyse, a task that has already begun. Another interesting research direction that came up from this work is the temporal enrichment analysis of epigenomics data across different developmental stages, in which changes of epigenomic enrichment suggest time-specific and tissue-specific functional gene and gene isoforms regulation.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

An important quest in genomics since the publication of the first complete human genome in 2003 has been its functional annotation. DNA holds the instructions to the production of the components necessary for the life of cells and organisms. A complete functional catalog of genomic regions will help the understanding of the cell body and its dynamics, thus creating links between genotype and phenotypic traits. The need for annotations prompted the development of several bioinformatic methods. In the context of promoter and first exon predictors, the majority of models relies principally on structural and chemical properties of the DNA sequence. Some of them integrate information from epigenomic and transcriptomic data as secondary features. Current genomic research asserts that reference genome annotations are far from being fully annotated (human organism included).Physicians rely on reference genome annotations and functional databases to understand disorders with genetic basis, and missing annotations may lead to unresolved cases. Because of their complexity, neurodevelopmental disorders are under study to figure out all genetic regions that are involved. Besides functional validation on model organisms, the search for genotype-phenotype association is supported by statistical analysis, which is typically biased towards known functional regions.This thesis addresses the use of an in-silico integrative analysis to improve reference genome annotations and discover novel functional regions associated with neurodevelopemental disorders. The contributions outlined in this document have practical applications in clinical settings. The presented bioinformatic method is based on epigenomic and transcriptomic data, thus excluding features from DNA sequence. Such integrative approach applied on brain data allowed the discovery of two novel promoters and coding first exons in the human DLG2 gene, which were also found to be statistically associated with neurodevelopmental disorders and intellectual disability in particular. The application of the same methodology to the whole genome resulted in the discovery of other novel exons expressed in brain. Concerning the in-silico method itself, the research demanded a high number of functional and clinical datasets to properly support and validate our discoveries.This work describes a bioinformatic method for genome annotation, in the specific area of promoter and first exons. So far the method has been applied on brain data, and the extension to the whole body data would be a logical by-product. We will leverage distributed frameworks to tackle the even higher amount of data to analyse, a task that has already begun. Another interesting research direction that came up from this work is the temporal enrichment analysis of epigenomics data across different developmental stages, in which changes of epigenomic enrichment suggest time-specific and tissue-specific functional gene and gene isoforms regulation.

Gazzo, Andrea

Beyond monogenic diseases: a first collection and analysis of digenic diseases PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/272617,
title = {Beyond monogenic diseases: a first collection and analysis of digenic diseases},
author = {Andrea Gazzo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/272617/5/ContratDiGazzo.pdf},
year = {2018},
date = {2018-01-01},
abstract = {In the next generation sequencing era many bioinformatics tools have been developed for assisting scientists in their studies on the molecular basis of genetic diseases, often with the aim of identifying the pathogenic variants. As a consequence, in the last decades more than one hundred new disease-gene associations have been discovered. Nevertheless, the genetic basis of many genetic diseases yet remains undisclosed. It has been shown that many diseases considered as monogenic with an imperfect genotype-phenotype correlation or incomplete penetrance are, on the contrary, caused or modulated by more than one mutated gene, meaning that they are in fact oligogenic. Current bioinformatics methods used for identifying pathogenic variants are trained and fine-tuned for identifying a single variant responsible of a disease. This monogenic-oriented approach cannot be used to explore the impact of combinations of variants in different genes on the complexity and genetic heterogeneity of rare diseases. Digenic diseases are the simplest form of oligogenic disease and thus they can provide a conceptual bridge between monogenic and the poorly understood polygenic diseases.The ambition of this thesis is to collect and analyse digenic data, introducing this topic in the bioinformatics field where digenic diseases are still an unexplored branch. This can be divided in two steps: the first consists in the creation of a central repository containing detailed information on digenic diseases; the second is an analysis of their peculiarities, using machine learning methods for studying subclasses of digenic effects.In the first step we developed DIDA (DIgenic diseases DAtabase), a novel database that provides for the first time a curated collection of genes and associated variants involved in digenic diseases. Detailed information related to the digenic mechanism have been manually mined from the medical literature. All instances in DIDA were also assigned to two sub classes of digenic effects, annotated as true digenic (both genes are required for developing the disease) and composite classes (one gene is sufficient to produce the disease phenotype, the second one alters it or change significantly the age of onset).In the second step, we hypothesized that the digenic effect may be related to some biological properties characterizing digenic combinations. Using machine learning methods, we show that a set of variant, gene and higher-level features can differentiate between the true digenic and composite classes with high accuracy. Moreover, we show that a digenic effect decision profile, extracted from the predictive model, motivates why an instance is assigned to either of the two classes.Together, our results show that digenic disease data generates novel insights, providing a glimpse into the oligogenic realm.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Reggiani, Claudio

Bioinformatic discovery of novel exons expressed in human brain and their association with neurodevelopmental disorders PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/270994,
title = {Bioinformatic discovery of novel exons expressed in human brain and their association with neurodevelopmental disorders},
author = {Claudio Reggiani},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/270994/5/ContratDiReggiani.pdf},
year = {2018},
date = {2018-01-01},
abstract = {An important quest in genomics since the publication of the first complete human genome in 2003 has been its functional annotation. DNA holds the instructions to the production of the components necessary for the life of cells and organisms. A complete functional catalog of genomic regions will help the understanding of the cell body and its dynamics, thus creating links between genotype and phenotypic traits. The need for annotations prompted the development of several bioinformatic methods. In the context of promoter and first exon predictors, the majority of models relies principally on structural and chemical properties of the DNA sequence. Some of them integrate information from epigenomic and transcriptomic data as secondary features. Current genomic research asserts that reference genome annotations are far from being fully annotated (human organism included).Physicians rely on reference genome annotations and functional databases to understand disorders with genetic basis, and missing annotations may lead to unresolved cases. Because of their complexity, neurodevelopmental disorders are under study to figure out all genetic regions that are involved. Besides functional validation on model organisms, the search for genotype-phenotype association is supported by statistical analysis, which is typically biased towards known functional regions.This thesis addresses the use of an in-silico integrative analysis to improve reference genome annotations and discover novel functional regions associated with neurodevelopemental disorders. The contributions outlined in this document have practical applications in clinical settings. The presented bioinformatic method is based on epigenomic and transcriptomic data, thus excluding features from DNA sequence. Such integrative approach applied on brain data allowed the discovery of two novel promoters and coding first exons in the human DLG2 gene, which were also found to be statistically associated with neurodevelopmental disorders and intellectual disability in particular. The application of the same methodology to the whole genome resulted in the discovery of other novel exons expressed in brain. Concerning the in-silico method itself, the research demanded a high number of functional and clinical datasets to properly support and validate our discoveries.This work describes a bioinformatic method for genome annotation, in the specific area of promoter and first exons. So far the method has been applied on brain data, and the extension to the whole body data would be a logical by-product. We will leverage distributed frameworks to tackle the even higher amount of data to analyse, a task that has already begun. Another interesting research direction that came up from this work is the temporal enrichment analysis of epigenomics data across different developmental stages, in which changes of epigenomic enrichment suggest time-specific and tissue-specific functional gene and gene isoforms regulation.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Fimereli, Danai

Computational analyses of gene fusions, viruses and parasitic genomic elements in breast cancer PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/263609,
title = {Computational analyses of gene fusions, viruses and parasitic genomic elements in breast cancer},
author = {Danai Fimereli},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/263609/5/ContratDanaiFimereli.pdf},
year = {2018},
date = {2018-01-01},
abstract = {Breast cancer is the most common cancer in women and research efforts to unravel the underlying mechanisms that drive carcinogenesis are continuous. The emergence of high-throughput sequencing techniques and their constant advancement, in combination with large scale studies of genomic and transcriptomic data, allowed the identification of important genetic changes that take place in the breast cancer genome, including somatic mutations, copy number aberrations and genomic rearrangements.The overall aim of this thesis is to explore the presence of genetic changes that take place in the breast cancer transcriptome and their possible contribution to carcinogenesis. The aim of the first research study was the identification of expressed gene fusions in breast cancer and the study of their association with other genomic events. For achieving this, transcriptome sequencing and Single Nucleotide Polymorphism arrays data for a cohort of 55 tumors and 10 normal breast tissues were combined. Gene fusions were detected in the majority of the samples, with evident differences between breast cancer subtypes, where HER2+ samples had significantly more fusions than the other subtypes. The genome-wide analysis uncovered localization of fusion genes in specific chromosomes like 17, 8 or 20. Additionally, a positive correlation between the number of gene fusions and the number of amplifications was observed, including the association between fusions on chromosome 17 and the amplifications in HER2+ samples, which can be attributed to the highly rearranged genomes of these subtypes. Finally, the absence of highly recurrent fusions across this cohort adds to the notion that gene fusions in breast cancer are most likely private events, with the majority being “passenger” events. In the second research study, the aim was to identify a connection between viral infections and breast cancer by devising five different computational methods for the analysis of both transcriptome and exome data in a cohort of 58 breast tumors. Despite being able to detect viral sequences in our testing dataset, no significantly high numbers of viral sequences were detected in our samples. Specifically, viral sequences (~2-30 reads) were extracted belonging to viruses EBV, HHV6 and Merkel cell polyomavirus. Such low levels of viral expression direct against a viral etiology for breast cancer but one should not exclude possible cases of integrated but silent viruses.In the third research project, we analyzed in silico the transcriptional profiles of human endogenous retroviruses in breast cancer. Despite being scattered across the genome in large numbers, a number of ERVs are actively transcribed, consisting of a small percentage of the total mapped reads. Alongside protein coding genes and lncRNAs, they show distinct expression profiles across the different breast cancer subtypes with luminal and basal-like samples clear separating from each other. Additionally, distinct profiles between ER+ and ER- samples were observed. Tumor specific ERV loci show an association with the immune status of the tumors, indicating that ERVs are reactivated in tumors and could play a role in the activation of the immune response cascade.The results presented in this thesis exhibit only in a small fragment the diversity and heterogeneity of the breast cancer transcriptome. The strength of the sequencing techniques allows the in depth detection of different genomic events. Gene fusions should be considered as part of the breast cancer transcriptome but their low recurrence across samples indicates for a role as passenger events. Under the light of existing results, viral infections do not play a significant role in breast cancer. On the other hand, human endogenous retroviruses, despite originating from exogenous viruses, seems to exhibit transcriptional profiles similar to those of normal genes, indicating that they are part of the genome’s transcriptional machinery.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Breast cancer is the most common cancer in women and research efforts to unravel the underlying mechanisms that drive carcinogenesis are continuous. The emergence of high-throughput sequencing techniques and their constant advancement, in combination with large scale studies of genomic and transcriptomic data, allowed the identification of important genetic changes that take place in the breast cancer genome, including somatic mutations, copy number aberrations and genomic rearrangements.The overall aim of this thesis is to explore the presence of genetic changes that take place in the breast cancer transcriptome and their possible contribution to carcinogenesis. The aim of the first research study was the identification of expressed gene fusions in breast cancer and the study of their association with other genomic events. For achieving this, transcriptome sequencing and Single Nucleotide Polymorphism arrays data for a cohort of 55 tumors and 10 normal breast tissues were combined. Gene fusions were detected in the majority of the samples, with evident differences between breast cancer subtypes, where HER2+ samples had significantly more fusions than the other subtypes. The genome-wide analysis uncovered localization of fusion genes in specific chromosomes like 17, 8 or 20. Additionally, a positive correlation between the number of gene fusions and the number of amplifications was observed, including the association between fusions on chromosome 17 and the amplifications in HER2+ samples, which can be attributed to the highly rearranged genomes of these subtypes. Finally, the absence of highly recurrent fusions across this cohort adds to the notion that gene fusions in breast cancer are most likely private events, with the majority being “passenger” events. In the second research study, the aim was to identify a connection between viral infections and breast cancer by devising five different computational methods for the analysis of both transcriptome and exome data in a cohort of 58 breast tumors. Despite being able to detect viral sequences in our testing dataset, no significantly high numbers of viral sequences were detected in our samples. Specifically, viral sequences (~2-30 reads) were extracted belonging to viruses EBV, HHV6 and Merkel cell polyomavirus. Such low levels of viral expression direct against a viral etiology for breast cancer but one should not exclude possible cases of integrated but silent viruses.In the third research project, we analyzed in silico the transcriptional profiles of human endogenous retroviruses in breast cancer. Despite being scattered across the genome in large numbers, a number of ERVs are actively transcribed, consisting of a small percentage of the total mapped reads. Alongside protein coding genes and lncRNAs, they show distinct expression profiles across the different breast cancer subtypes with luminal and basal-like samples clear separating from each other. Additionally, distinct profiles between ER+ and ER- samples were observed. Tumor specific ERV loci show an association with the immune status of the tumors, indicating that ERVs are reactivated in tumors and could play a role in the activation of the immune response cascade.The results presented in this thesis exhibit only in a small fragment the diversity and heterogeneity of the breast cancer transcriptome. The strength of the sequencing techniques allows the in depth detection of different genomic events. Gene fusions should be considered as part of the breast cancer transcriptome but their low recurrence across samples indicates for a role as passenger events. Under the light of existing results, viral infections do not play a significant role in breast cancer. On the other hand, human endogenous retroviruses, despite originating from exogenous viruses, seems to exhibit transcriptional profiles similar to those of normal genes, indicating that they are part of the genome’s transcriptional machinery.

Dierckxsens, Nicolas

Targeted organelle genome assembly and heteroplamsy detection PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/277507,
title = {Targeted organelle genome assembly and heteroplamsy detection},
author = {Nicolas Dierckxsens},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/277507/5/ContratDiDierckxsens.pdf},
year = {2018},
date = {2018-01-01},
abstract = {Thanks to the development of next-generation sequencing (NGS) technology, whole genome data can be readily obtained from a variety of samples. Since the massive increase in available sequencing data, the development of efficient assembly algorithms has become the new bottleneck. Almost every new released tool is based on the De Brujin graph method, which focuses on assembling complete datasets with mathematical models. Although the decreasing sequencing costs made whole genome sequencing (WGS) the most straightforward and least laborious approach of gathering sequencing data, many research projects are only interested in the extranuclear genomes. Unfortunately, few of the available tools are specifically designed to efficiently retrieve these extranuclear genomes from WGS datasets. We developed a seed-and-extend algorithm that assembles organelle circular genomes from WGS data, starting from a single short seed sequence. The algorithm has been tested on several new (Gonioctena intermedia and Avicennia marina) and public (Arabidopsis thaliana and Oryza sativa) whole genome Illumina datasets and always outperformed other assemblers in assembly accuracy and contiguity. In our benchmark, NOVOPlasty assembled all genomes in less than 30 minutes with a maximum RAM memory requirement of 16 GB. NOVOPlasty is the only de novo assembler that provides a fast and straightforward manner to extract the extranuclear sequences from WGS data and generates one circular high quality contig.Heteroplasmy, the existence of multiple mitochondrial haplotypes within an individual, has been researched across different fields. Mitochondrial genome polymorphisms have been linked to multiple severe disorders and are of interest to evolutionary studies and forensic science. By utilizing ultra-deep sequencing, it is now possible to uncover previously undiscovered patterns of intra-individual polymorphism. However, it remains challenging to determine its source. Current available software can detect polymorphic sites but are not capable of determining the link between them. We therefore developed a new method to not only detect intra-individual polymorphisms within mitochondrial and chloroplast genomes, but also to look for linkage among polymorphic sites by assembling the sequence around each detected polymorphic site. Our benchmark study shows that this method can detect heteroplasmy more accurately than any method previously available and is the first tool that is able to completely or partially reconstruct the origin sequences for each intra-individual polymorphism.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Thanks to the development of next-generation sequencing (NGS) technology, whole genome data can be readily obtained from a variety of samples. Since the massive increase in available sequencing data, the development of efficient assembly algorithms has become the new bottleneck. Almost every new released tool is based on the De Brujin graph method, which focuses on assembling complete datasets with mathematical models. Although the decreasing sequencing costs made whole genome sequencing (WGS) the most straightforward and least laborious approach of gathering sequencing data, many research projects are only interested in the extranuclear genomes. Unfortunately, few of the available tools are specifically designed to efficiently retrieve these extranuclear genomes from WGS datasets. We developed a seed-and-extend algorithm that assembles organelle circular genomes from WGS data, starting from a single short seed sequence. The algorithm has been tested on several new (Gonioctena intermedia and Avicennia marina) and public (Arabidopsis thaliana and Oryza sativa) whole genome Illumina datasets and always outperformed other assemblers in assembly accuracy and contiguity. In our benchmark, NOVOPlasty assembled all genomes in less than 30 minutes with a maximum RAM memory requirement of 16 GB. NOVOPlasty is the only de novo assembler that provides a fast and straightforward manner to extract the extranuclear sequences from WGS data and generates one circular high quality contig.Heteroplasmy, the existence of multiple mitochondrial haplotypes within an individual, has been researched across different fields. Mitochondrial genome polymorphisms have been linked to multiple severe disorders and are of interest to evolutionary studies and forensic science. By utilizing ultra-deep sequencing, it is now possible to uncover previously undiscovered patterns of intra-individual polymorphism. However, it remains challenging to determine its source. Current available software can detect polymorphic sites but are not capable of determining the link between them. We therefore developed a new method to not only detect intra-individual polymorphisms within mitochondrial and chloroplast genomes, but also to look for linkage among polymorphic sites by assembling the sequence around each detected polymorphic site. Our benchmark study shows that this method can detect heteroplasmy more accurately than any method previously available and is the first tool that is able to completely or partially reconstruct the origin sequences for each intra-individual polymorphism.

Carcillo, Fabrizio

Beyond Supervised Learning in Credit Card Fraud Detection: A Dive into Semi-supervised and Distributed Learning PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/272119,
title = {Beyond Supervised Learning in Credit Card Fraud Detection: A Dive into Semi-supervised and Distributed Learning},
author = {Fabrizio Carcillo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/272119/5/ContratDiCarcillo.pdf},
year = {2018},
date = {2018-01-01},
abstract = {The expansion of the electronic commerce, as well as the increasing confidence of customers in electronic payments, makes of fraud detection a critical issue. The design of a prompt and accurate Fraud Detection System is a priority for many organizations in the business of credit cards. In this thesis we present a series of studies to increase the precision and the speed of fraud detection system. The thesis has three main contributions. The first concerns the integration of unsupervised techniques and supervised classifiers. We proposed several approaches to integrate outlier scores in the detection process and we found that the accuracy of a conventional classifier may be improved when information about the input distribution is used to augment the training set.The second contribution concerns the role of active learning in Fraud Detection. We have extensively compared several state-of-the-art techniques and found that Stochastic Semi-supervised Learning is a convenient approach to tackle the Selection Bias problem in the active learning process.The third contribution of the thesis is the design, implementation and assessment of SCARFF, an original framework for near real-time Streaming Fraud Detection. This framework integrates Big Data technology (notably tools like Kafka, Spark and Cassandra) with a machine learning approach to deal with imbalance, non-stationarity and feedback latency in a scalable manner. Experimental results on a massive dataset of real credit card transactions have showed that our framework is scalable, efficient and accurate over a big stream of transactions.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Porretta'S, Luciano

MODELS AND METHODS IN GENOME WIDE ASSOCIATION STUDIES PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | BibTeX

Bizet, Martin

Bioinformatic inference of a prognostic epigenetic signature of immunity in breast cancers PhD Thesis

2018, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/265092,
title = {Bioinformatic inference of a prognostic epigenetic signature of immunity in breast cancers},
author = {Martin Bizet},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/265092/7/ContratDiBizet.pdf},
year = {2018},
date = {2018-01-01},
abstract = {L’altération des marques épigénétiques est de plus en plus reconnue comme une caractéristique fondamentale des cancers. Dans cette th`ese, nous avons utilisé des profils de méthylation de l’ADN en vue d’améliorer la classification des patients atteints du cancer du sein gr^ace `a une approche basée sur l’apprentissage automatique. L’objectif `a long terme est le développement d’outils cliniques de médecine personnalisée. Les données de méthylation de l’ADN furent acquises `a l’aide d’une puce `a ADN dédiée `a la méthylation, appelée Infinium. Cette technologie est récente comparée, par exemple, aux puces d’expression génique et son prétraitement n’est pas encore standardisé. La premi`ere partie de cette th`ese fut donc consacrée `a l’évaluation des méthodes de normalisation par comparaison des données normalisées avec d’autres technologies (pyroséquenccage et RRBS) pour les deux technologies Infinium les plus récentes (450k et 850k). Nous avons également évalué la couverture de régions biologiquement relevantes (promoteurs et amplificateurs) par les deux technologies. Ensuite, nous avons utilisé les données Infinium (correctement prétraitées) pour développer un score, appelé MeTIL score, qui présente une valeur pronostique et prédictive dans les cancers du sein. Nous avons profité de la capacité de la méthylation de l’ADN `a refléter la composition cellulaire pour extraire une signature de méthylation (c’est-`a-dire un ensemble de positions de l’ADN o`u la méthylation varie) qui refl`ete la présence de lymphocytes dans l’échantillon tumoral. Apr`es une sélection de sites présentant une méthylation spécifique aux lymphocytes, nous avons développé une approche basée sur l’apprentissage automatique pour obtenir une signature d’une tailleoptimale réduite `a cinq sites permettant potentiellement une utilisation en clinique. Apr`es conversion de cette signature en un score, nous avons montré sa spécificité pour les lymphocytes `a l’aide de données externes et de simulations informatiques. Puis, nous avons montré la capacité du MeTIL score `a prédire la réponse `a la chimiothérapie ainsi que son pouvoir pronostique dans des cohortes indépendantes de cancer du sein et, m^eme, dans d’autres cancers.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

2017

Huculeci, Radu Ion; Kieken, Fabien; Garcia-Pino, Abel; Buts, Lieven; van Nuland, Nico A J; Lenaerts, Tom

Structural characterization of monomeric/dimeric state of p59fyn SH2 domain Book Chapter

In: 1555 , pp. 555, Humana Press, 2017, (Language of publication: fr).

Links | BibTeX

Kieken, Fabien; Loth, Karine; van Nuland, Nico A J; Tompa, Peter; Lenaerts, Tom

Chemical shift assignments of the partially deuterated Fyn SH2-SH3 domain. Journal Article

In: Biomolecular NMR assignments, 2017, (DOI: 10.1007/s12104-017-9792-1).

Abstract | Links | BibTeX

Gazzo, Andrea; Raimondi, Daniele; Daneels, Dorien; Moreau, Yves; Smits, Guillaume; Dooren, Sonia Van; Lenaerts, Tom

Understanding mutational effects in digenic diseases Journal Article

In: Nucleic acids research, 45 (15), 2017, (DOI: 10.1093/nar/gkx557).

Abstract | Links | BibTeX

Raimondi, Daniele; cc, Ibrahim Tanyal; Ferte, Julien; Gazzo, Andrea; Orlando, Gabriele; Lenaerts, Tom; Rooman, Marianne; Vranken, Wim F

DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Journal Article

In: Nucleic acids research, 45 (W1), pp. W201-W206, 2017, (DOI: 10.1093/nar/gkx390).

Abstract | Links | BibTeX

Byrski, Aleksander; Świderska, Ewelina; Łasisz, Jakub; Kisiel-Dorohinicki, Marek; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin; Nowe, Ann

Socio-cognitively inspired ant colony optimization Journal Article

In: Journal of Computational Science, 21 , pp. 397-406, 2017, (DOI: 10.1016/j.jocs.2016.10.010).

Abstract | Links | BibTeX

Reggiani, Claudio; Coppens, Sandra; Sekhara, Tayeb; Dimov, Ivan; Pichon, Bruno; Lufin, Nicolas; Addor, Marie Claude; Belligni, Elga Fabia; Digilio, Maria Cristina; Faletra, Flavio; Ferrero, Giovanni Battista; Gerard, Marion; Isidor, Bertrand; Joss, Shelagh; Niel-Bütschi, Florence; Perrone, Maria Dolores; Petit, Florence; Renieri, Alessandra; Romana, Serge; Topa, Alexandra; Vermeesch, Joris Robert; Lenaerts, Tom; Casimir, Georges; Abramowicz, Marc; Bontempi, Gianluca; Vilain, Catheline; Deconinck, Nicolas; Smits, Guillaume

Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability. Journal Article

In: Genome medicine, 9 (1), pp. 67, 2017, (DOI: 10.1186/s13073-017-0452-y).

Abstract | Links | BibTeX

Cava, Claudia; Colaprico, Antonio; Bertoli, Gloria; Graudenzi, Alex; Silva, Tiago C; Olsen, Catharina; Noushmehr, Houtan; Bontempi, Gianluca; Mauri, Giancarlo; Castiglioni, Isabella

SpidermiR: an R/Bioconductor package for integrative analysis with miRNA data Journal Article

In: International journal of molecular sciences, 2017, (DOI: 10.3390/ijms18020274).

Abstract | Links | BibTeX

Orlando, Gabriele; Raimondi, Daniele; Khanna, T; Lenaerts, Tom; Vranken, Wim F

SVM-dependent pairwise HMM: an application to Protein pairwise alignments. Journal Article

In: Bioinformatics, 2017, (DOI: 10.1093/bioinformatics/btx391).

Abstract | Links | BibTeX

Carcillo, Fabrizio; Pozzolo, Andrea Dal; "e, Yann-A; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca

SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark Journal Article

In: Information fusion, 2017, (DOI: 10.1016/j.inffus.2017.09.005).

Abstract | Links | BibTeX

Martinez-Vaquero, Luis L A; Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

When agreement-accepting free-riders are a necessary evil for the evolution of cooperation. Journal Article

In: Scientific reports, 7 (1), pp. 2478, 2017, (DOI: 10.1038/s41598-017-02625-z).

Abstract | Links | BibTeX

Pereira, Luís Moniz; Lenaerts, Tom; Martinez-Vaquero, Luis L A; Han, The Anh T A H

Social manifestation of guilt leads to stable cooperation in multi-agent systems Journal Article

In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 3 , pp. 1421-1430, 2017, (Language of publication: en).

Abstract | Links | BibTeX

@article{info:hdl:2013/271395,
title = {Social manifestation of guilt leads to stable cooperation in multi-agent systems},
author = {Luís Moniz Pereira and Tom Lenaerts and Luis L A Martinez-Vaquero and The Anh T A H Han},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/271395},
year = {2017},
date = {2017-01-01},
journal = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS},
volume = {3},
pages = {1421-1430},
abstract = {Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.},
note = {Language of publication: en},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Brown, David Norman

Application of phylogenetic inference methods to quantify intra-tumour heterogeneity and evolution of breast cancers PhD Thesis

2017, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/260251,
title = {Application of phylogenetic inference methods to quantify intra-tumour heterogeneity and evolution of breast cancers},
author = {David Norman Brown},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/260251/6/ContratDavidBrown.pdf},
year = {2017},
date = {2017-01-01},
abstract = {Cancer related mortality is almost always due to metastatic dissemination of the primary disease. While research into the biological mechanisms that drive the metastatic cascade continues to unravel its molecular underpinnings, progress in our understanding of biological phenomena such as tumour heterogeneity and its relevance to the origins of distant recurrence or the emergence of resistance to therapy has been limited.In parallel to major breakthroughs in the development of high throughput molecular techniques, researchers have begun to utilise next generation sequencing to explore the relationship between primary and matched metastatic tumours in diverse types of neoplasia. Despite small cohort sizes and often, a limited number of matched metastases for each patient, pioneering studies have uncovered hitherto unknown biological processes such as the occurrence of organ specific metastatic lineages, polyclonal seeding and homing of metastatic cells to the primary tumour bed. While yet other studies continue to highlight the potential of genomic analyses, at the time this thesis was started, an in-depth knowledge of disease progression and metastatic dissemination was currently lacking in breast cancers.Herein, we employed phylogenetic inference methods to investigate intra-tumour heterogeneity and evolution of breast cancers. A combination of whole exome sequencing, custom ultra-deep resequencing and copy number profiling were applied to primary tumours and their associated metastases from ten autopsied breast cancer patients. Two modes of metastatic progression were observed. In the majority of cases, all distant metastases clustered on a branch separate from their primary lesion. Clonal frequency analysis of somatic mutations showed that the metastases had a monoclonal origin and descended from a common metastatic precursor. Alternatively, the primary tumour was clustered alongside metastases with early branches leading to distant organs. This dichotomy coincided with the clinical history of the patients whereby multiple seeding events from the primary tumour alongside cascading metastasis-to-metastasis disseminations occurred in treatment na"ive de novo metastatic patients, whereas descent from a common metastatic precursor was observed in patients who underwent primary surgery followed by systemic treatment. The data also showed that a distant metastasis can be horizontally cross-seeded and finally revealed a correlation between the extent of somatic point mutations private to the distant lesions and patient overall survival. In an unrelated dataset of relapsed breast cancer patients with matched primary and distant lesions profiled using whole genome sequencing, the landscape of somatic alterations confirmed the time dependency of copy number aberrations implying that cancer phylogenies can be dated using a molecular clock.The work presented here harnesses the strength of high throughput genomic techniques and state of the art phylogenetic tools to tell the evolutionary history of breast cancers. Our results show that the linear and parallel models of metastatic dissemination which have been held as near doctrines for many years are overstated point of views of cancer progression. Beyond the biological insights, these results suggest that surgical excision of the primary tumour in de novo metastatic breast cancers might reduce dissemination in selected cases hence providing a potential biological rationale for this practice. Similarly, there is no strong evidence of benefit in overall survival from surgical resection of oligo-metastases in breast cancer. From our analyses, metastatic lesions constitute an additional source of seeding and heterogeneity in advanced breast cancer. The data presented here is too small to derive practice-changing evidence, but supports the concept that resecting isolated metastases may be of clinical benefit in oligo-metastatic breast cancer patients. In both cases, results from larger prospective studies are warranted.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Cancer related mortality is almost always due to metastatic dissemination of the primary disease. While research into the biological mechanisms that drive the metastatic cascade continues to unravel its molecular underpinnings, progress in our understanding of biological phenomena such as tumour heterogeneity and its relevance to the origins of distant recurrence or the emergence of resistance to therapy has been limited.In parallel to major breakthroughs in the development of high throughput molecular techniques, researchers have begun to utilise next generation sequencing to explore the relationship between primary and matched metastatic tumours in diverse types of neoplasia. Despite small cohort sizes and often, a limited number of matched metastases for each patient, pioneering studies have uncovered hitherto unknown biological processes such as the occurrence of organ specific metastatic lineages, polyclonal seeding and homing of metastatic cells to the primary tumour bed. While yet other studies continue to highlight the potential of genomic analyses, at the time this thesis was started, an in-depth knowledge of disease progression and metastatic dissemination was currently lacking in breast cancers.Herein, we employed phylogenetic inference methods to investigate intra-tumour heterogeneity and evolution of breast cancers. A combination of whole exome sequencing, custom ultra-deep resequencing and copy number profiling were applied to primary tumours and their associated metastases from ten autopsied breast cancer patients. Two modes of metastatic progression were observed. In the majority of cases, all distant metastases clustered on a branch separate from their primary lesion. Clonal frequency analysis of somatic mutations showed that the metastases had a monoclonal origin and descended from a common metastatic precursor. Alternatively, the primary tumour was clustered alongside metastases with early branches leading to distant organs. This dichotomy coincided with the clinical history of the patients whereby multiple seeding events from the primary tumour alongside cascading metastasis-to-metastasis disseminations occurred in treatment na"ive de novo metastatic patients, whereas descent from a common metastatic precursor was observed in patients who underwent primary surgery followed by systemic treatment. The data also showed that a distant metastasis can be horizontally cross-seeded and finally revealed a correlation between the extent of somatic point mutations private to the distant lesions and patient overall survival. In an unrelated dataset of relapsed breast cancer patients with matched primary and distant lesions profiled using whole genome sequencing, the landscape of somatic alterations confirmed the time dependency of copy number aberrations implying that cancer phylogenies can be dated using a molecular clock.The work presented here harnesses the strength of high throughput genomic techniques and state of the art phylogenetic tools to tell the evolutionary history of breast cancers. Our results show that the linear and parallel models of metastatic dissemination which have been held as near doctrines for many years are overstated point of views of cancer progression. Beyond the biological insights, these results suggest that surgical excision of the primary tumour in de novo metastatic breast cancers might reduce dissemination in selected cases hence providing a potential biological rationale for this practice. Similarly, there is no strong evidence of benefit in overall survival from surgical resection of oligo-metastases in breast cancer. From our analyses, metastatic lesions constitute an additional source of seeding and heterogeneity in advanced breast cancer. The data presented here is too small to derive practice-changing evidence, but supports the concept that resecting isolated metastases may be of clinical benefit in oligo-metastatic breast cancer patients. In both cases, results from larger prospective studies are warranted.

Jeschke, Jana; Bizet, Martin; Desmedt, Christine; Calonne, Emilie; Dedeurwaerder, Sarah; Garaud, Soizic; Koch, Alexander K; Larsimont, Denis; Salgado, Roberto; den Eynden, Gert Van; Willard-Gallo, Karen; Bontempi, Gianluca; Defrance, Matthieu; Sotiriou, Christos; cc, Fran

DNA methylation-based immune response signature improves patient diagnosis in multiple cancers. Journal Article

In: The Journal of clinical investigation, 127 (8), pp. 3090-3102, 2017, (DOI: 10.1172/JCI91095).

Abstract | Links | BibTeX

Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability. Journal Article

In: Genome medicine, 9 (1), pp. 67, 2017, (DOI: 10.1186/s13073-017-0452-y).

Abstract | Links | BibTeX

Pham, Ngoc Cam; Haibe-Kains, Benjamin; Bellot, Pau; Bontempi, Gianluca; Meyer, Patrick E

Study of Meta-analysis strategies for network inference using information-theoretic approaches Journal Article

In: BioData Mining, 10 (1), 2017, (DOI: 10.1186/s13040-017-0136-6).

Abstract | Links | BibTeX

@article{info:hdl:2013/259607,
title = {Study of Meta-analysis strategies for network inference using information-theoretic approaches},
author = {Ngoc Cam Pham and Benjamin Haibe-Kains and Pau Bellot and Gianluca Bontempi and Patrick E Meyer},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/259607},
year = {2017},
date = {2017-01-01},
journal = {BioData Mining},
volume = {10},
number = {1},
abstract = {Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets. To date, there are mainly two strategies for the problem of interest: the first one (“data merging”) merges all datasets together and then infers a GRN whereas the other (“networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. Results: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. Conclusions: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones.},
note = {DOI: 10.1186/s13040-017-0136-6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Pham, Ngoc Cam; Haibe-Kains, Benjamin; Bellot, Pau; Bontempi, Gianluca; Meyer, Patrick E

Study of Meta-analysis Strategies for Network Inference Using Information-Theoretic Approaches Journal Article

In: Proceedings - International Workshop on Database and Expert Systems Applications, pp. 76-83, 2017, (DOI: 10.1109/DEXA.2016.030).

Abstract | Links | BibTeX

@article{info:hdl:2013/247710,
title = {Study of Meta-analysis Strategies for Network Inference Using Information-Theoretic Approaches},
author = {Ngoc Cam Pham and Benjamin Haibe-Kains and Pau Bellot and Gianluca Bontempi and Patrick E Meyer},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/247710},
year = {2017},
date = {2017-01-01},
journal = {Proceedings - International Workshop on Database and Expert Systems Applications},
pages = {76-83},
abstract = {Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge insystems biology. Thanks to high-throughput technologies, amassive amount of gene-expression data has been accumulatedin the public repositories. Modelling GRNs from multipleexperiments (also called integrative analysis) has, therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robustthan the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a smallnumber of samples. To date, there are mainly two strategies for the problemof interest: the first one ('data merging') merges all datasetstogether and then infers a GRN whereas the other ('networksensemble') infers GRNs from every dataset separately and thenaggregates them using some ensemble rules (such as ranksumor weightsum). Unfortunately, a thorough comparison of thesetwo approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set ofexperiments based on in silico benchmarks. Furthermore, wepresent a new meta-analysis approach for inferring GRNs frommultiple studies. Our proposed approach, adapted to methodsbased on pairwise measures such as correlation or mutualinformation, consists of two steps: aggregating matrices of thepairwise measures from every dataset followed by extractingthe network from the meta-matrix.},
note = {DOI: 10.1109/DEXA.2016.030},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Pozzolo, Andrea Dal; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy Journal Article

In: IEEE Transactions on Neural Networks and Learning Systems, 99 , 2017, (DOI: 10.1109/TNNLS.2017.2736643).

Abstract | Links | BibTeX

@article{info:hdl:2013/258224,
title = {Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy},
author = {Andrea Dal Pozzolo and Giacomo Boracchi and Olivier Caelen and Cesare Alippi and Gianluca Bontempi},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/258224},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
volume = {99},
abstract = {Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.},
note = {DOI: 10.1109/TNNLS.2017.2736643},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Xu, Taosheng; Le, Thuc Duy; Liu, Lin; Su, Ning; Wang, Rujing; Sun, Bingyu; Colaprico, Antonio; Bontempi, Gianluca; Li, Jiuyong

CancerSubtypes: An R/Bioconductor package for molecular cancer subtype identification, validation and visualization Journal Article

In: Bioinformatics, 33 (19), pp. 3131-3133, 2017, (DOI: 10.1093/bioinformatics/btx378).

Abstract | Links | BibTeX

Stefani, Jacopo De; Caelen, Olivier; Hattab, Dalila; Bontempi, Gianluca

Machine learning for multi-step ahead forecasting of volatility proxies Journal Article

In: CEUR Workshop Proceedings, 1941 , pp. 17-28, 2017, (Language of publication: en).

Abstract | Links | BibTeX

Raimondi, Daniele

The effect of genome variation on human proteins: understanding variants and improving their deleteriousness prediction through extensive contextualisation PhD Thesis

2017, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/251313b,
title = {The effect of genome variation on human proteins: understanding variants and improving their deleteriousness prediction through extensive contextualisation},
author = {Daniele Raimondi},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/251313/5/ContratDiRaimondi.pdf},
year = {2017},
date = {2017-01-01},
abstract = {Rapid technological advances are providing unprecedented insights in the biologicalsciences, with massive amounts of data generated on genomic and protein sequences.These data continue to grow exponentially, and they are extremely valuable for com-putational tools where the effect of genomic variants on human health is predicted.State of the art tools in this field give varying results and only tend to agree in thecase of single variants that are strongly correlated to disease. The aim of this workis to increase the reliability of these methods, as well as our understanding of theunderlying biological mechanisms that lead to disease. We first developed machinelearning (ML) based structural bioinformatics predictors that are able to predictmolecular features of proteins from the sequence alone. We then used these tools forin silico analysis of the molecular effects of known variants on the affected proteins,and integrated these data with other sources heterogenous sources of information,such as the essentiality of a gene, that put the variants into their broader biologicalcontext. With this information we created DEOGEN, a novel predictor in this field,which is able to deal with the two most common forms of genomic variation, namelySingle Nucleotide Variants (SNVs) and short Insertions and DELetions (INDELs).DEOGEN performs at least on par with other state of the art methods in this fieldon different datasets. The method was then extended with additional contextualdata and is now available as DEOGEN2 via a web server, which visualizes the pre-dicted results for all variants in most human proteins through an interactive interfacetargeted to both bioinformaticians and clinicians.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Han, The Anh T A H; Pereira, Luís Moniz; Martinez-Vaquero, Luis L A; Lenaerts, Tom

Centralized vs. Personalized Commitments and their influence on Cooperation in Group Interactions Inproceedings

In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), 2017, (Conference: 31st AAAI Conference on Artificial Intelligence (AAAI-17)(4-9 February 2017: San Francisco, USA)).

Links | BibTeX

Fernandez-Domingos, Elias; Burguillo-Rial, Juan C; Lenaerts, Tom

Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game Inproceedings

In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), 2017, (Conference: 31st AAAI Conference on Artificial Intelligence (AAAI-17)(4-9 February 2017: San Francisco, USA)).

Links | BibTeX

Fernandez-Domingos, Elias; Burguillo-Rial, Juan C; Nowe, Ann; Lenaerts, Tom

Coordinating Human and Agent Behavior in Collective-Risk Scenarios Inproceedings

In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), 2017, (Conference: 31st AAAI Conference on Artificial Intelligence (AAAI-17)(4-9 February 2017: San Francisco, USA)).

Links | BibTeX

Lenaerts, Tom; Han, The Anh T A H; Pereira, Luis Moniz; Martinez-Vaquero, Luis A

When apology is sincere, cooperation evolves, even when mistakes occur frequently Inproceedings

In: Proceedings of the AISB Annual Convention, Symposium on Computational Modelling of Emotion: Theory and Applications, pp. 193-195, 2017, (Language of publication: en).

Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Martinez-Vaquero, Luis A; Lenaerts, Tom

Evolution of commitment and level of participation in public goods games. Inproceedings

In: Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1431-1432, 2017, (Language of publication: en).

Links | BibTeX

Pereira, Luis Moniz; Lenaerts, Tom; Martinez-Vaquero, Luis A; others,

Social manifestation of guilt leads to stable cooperation in multi-agent systems Inproceedings

In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems(AAMAS), pp. 1422-1430, 2017, (Language of publication: en).

Links | BibTeX

Pereira, Luis Moniz; Lenaerts, Tom; Han, The Anh T A H; Martinez-Vaquero, Luis A

Evolutionary Game Theory Modelling of Guilt Inproceedings

In: Proceedings of the AISB Annual Convention, Symposium on Computational Modelling of Emotion: Theory and Applications, pp. 189-192, 2017, (Language of publication: en).

Links | BibTeX

`e, Nathaniel Mon P; Lenaerts, Tom; Pacheco, Jorge M J M; Dingli, David

Evolutionary Dynamics of Paroxysmal Nocturnal Hemoglobinuria Miscellaneous

2017, (Conference: Benelux Bioinformatics Conference 2017).

Links | BibTeX

Grujić, Jelena; Lenaerts, Tom

Looking for the strategies in the repeated prisoner's dilemma when the cooperation is established Miscellaneous

2017, (Conference: 3rd International Conference of Computational Social Science(07/2017: Cologne, Germany)).

Links | BibTeX

Gazzo, Andrea; Raimondi, Daniele; Daneels, Dorien; Moreau, Yves; Smits, Guillaume; Dooren, Sonia Van; Lenaerts, Tom

Understanding mutational effects in digenic diseases Miscellaneous

2017, (Conference: (21-25 juillet 2017: Prague, Tch`eque)).

Links | BibTeX

Raimondi, Daniele; cc, Ibrahim Tanyal; Ferte, Julien; Gazzo, Andrea; Orlando, Gabriele; Lenaerts, Tom; Rooman, Marianne; Vranken, Wim F

DEOGEN2: prediction and interactive visualisation of SingleAmino Acid Variant deleteriousness in human proteins Miscellaneous

2017, (Conference: (21-25 Juillet 2017: Prague, Tch`eque)).

Links | BibTeX

Grujić, Jelena; Lenaerts, Tom

Network influence on promotion of cooperation - Is there imitation? Miscellaneous

2017, (Conference: 8th Conference on Complex Networks.(03/2017: Dubrovnik, Croatia)).

Links | BibTeX

Smits, Guillaume; Gazzo, Andrea; Daneels, Dorien; Raimondi, Daniele; Papadimitriou, Sofia; Moreau, Yves; Dooren, Sonia Van; Lenaerts, Tom

Understanding combinatorial effects of variants using machine learning and DIDA, the DIgenic diseases Database Miscellaneous

2017, (Conference: Genomics on Rare Disease(Wellcome Genome Campus, Hinxton, Cambridge, UK)).

Links | BibTeX

Fernandez-Domingos, Elias; Burguillo-Rial, Juan C; Lenaerts, Tom

Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game Miscellaneous

2017, (Conference: 29nd Benelux conference on Artificial Intelligence(28-29 nov 2017: Groningen)).

Links | BibTeX

Raimondi, Daniele

The effect of genome variation on human proteins: understanding variants and improving their deleteriousness prediction through extensive contextualisation PhD Thesis

2017, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/251313,
title = {The effect of genome variation on human proteins: understanding variants and improving their deleteriousness prediction through extensive contextualisation},
author = {Daniele Raimondi},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/251313/5/ContratDiRaimondi.pdf},
year = {2017},
date = {2017-01-01},
abstract = {Rapid technological advances are providing unprecedented insights in the biologicalsciences, with massive amounts of data generated on genomic and protein sequences.These data continue to grow exponentially, and they are extremely valuable for com-putational tools where the effect of genomic variants on human health is predicted.State of the art tools in this field give varying results and only tend to agree in thecase of single variants that are strongly correlated to disease. The aim of this workis to increase the reliability of these methods, as well as our understanding of theunderlying biological mechanisms that lead to disease. We first developed machinelearning (ML) based structural bioinformatics predictors that are able to predictmolecular features of proteins from the sequence alone. We then used these tools forin silico analysis of the molecular effects of known variants on the affected proteins,and integrated these data with other sources heterogenous sources of information,such as the essentiality of a gene, that put the variants into their broader biologicalcontext. With this information we created DEOGEN, a novel predictor in this field,which is able to deal with the two most common forms of genomic variation, namelySingle Nucleotide Variants (SNVs) and short Insertions and DELetions (INDELs).DEOGEN performs at least on par with other state of the art methods in this fieldon different datasets. The method was then extended with additional contextualdata and is now available as DEOGEN2 via a web server, which visualizes the pre-dicted results for all variants in most human proteins through an interactive interfacetargeted to both bioinformaticians and clinicians.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

2016

Huculeci, Radu Ion; Cilia, Elisa; Lyczek, Agatha; Buts, Lieven; Houben, Klaartje; Seeliger, Markus MA; van Nuland, Nico A J; Lenaerts, Tom

Dynamically Coupled Residues within the SH2 Domain of FYN Are Key to Unlocking Its Activity. Journal Article

In: Structure, 24 (11), pp. 1947-1959, 2016, (DOI: 10.1016/j.str.2016.08.016).

Abstract | Links | BibTeX

Han, The Anh T A H; Lenaerts, Tom

A synergy of costly punishment and commitment in cooperation dilemmas Journal Article

In: Adaptive behavior, 24 (4), pp. 237-248, 2016, (DOI: 10.1177/1059712316653451).

Abstract | Links | BibTeX

Tomás, Gil Da Rocha

Gene Expression Markers of Proliferation and Differentiation in Cancer & The Extent of Prognostic Signals in the Cancer Transcriptome PhD Thesis

2016, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/235915,
title = {Gene Expression Markers of Proliferation and Differentiation in Cancer & The Extent of Prognostic Signals in the Cancer Transcriptome},
author = {Gil Da Rocha Tomás},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/235915/5/ContratGilDaRochaTomas.pdf},
year = {2016},
date = {2016-01-01},
abstract = {Le cancer est un groupe de maladies génétiques opérationnellement défini par uneprolifération cellulaire incontr^olée, impliquant une défaillance del'homeostasie de l'organisme. La recherche sur le cancer vise `a fournir desoutils diagnostics précis et des traitements ajustés pour chacune de cesmaladies. La technologie microarray permet la quantification de l'expression detous les produits de transcription du génome humain et constitue donc un outilpour mieux comprendre la nature polygénique du cancer. La technologiemicroarray permet `a la fois de découvrir de nouvelles classes de cancers et deprédire l'issue de maladie en fonction de profils d'expression préalables. Enoutre, l'utilisation de signatures d'expression géniques en tant que marqueursreprésentatifs de certains processus physiologiques moléculaires permetl'emploi de données microarray pour tester des hypoth`eses biologiques.Cette dissertation a deux objectifs: (a) établir la mesure dans laquelledes marqueurs d'expression génique de la différenciation et de la proliférationcellulaire peuvent contribuer `a la classification des maladies cancéreuses; et(b) d'évaluer l'étendue des signaux pronostiques dans les transcriptomescancéreux.Nous avons mis au point une méthode objective pour extraire des signatures dedifférentiation organe-spécifiques `a partir de données d'expression génique.Nous avons ensuite démontré qu'une signature génique de différentiationtissu-spécifique est capable de distinguer avec précision entre des sous-typeshistologiques de difficile classification dans un mod`ele thyro"idien. Ceci faitpreuve du potentiel valeur clinique et diagnostique des signatures dedifférentiation dans le domaine oncologique.Nous montrons aussi qu'une fraction non négligeable des transcriptomes cancéreuxest capable de prédire l'issue des respectives maladies, `a la suite d'uneanalyse systématique de 114 cohortes de profiles d'expression cancéreuxenglobant 19 types de cancers différents. Cet observation est probablement liée`a une vaste structure de corrélation parmis les profils d'expression cancéreux,partiellement expliquée par des variables techniques et biologiques. Cetteevidence met en cause l'utilisation généralisée d'associations statistiquesentre des marqueurs d'expression géniques et les issues de chaque maladie parmisplusieurs patients afin d'en déduire l'implication de mécanismes biologiquesparticuliers dans la progression du cancer.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Le cancer est un groupe de maladies génétiques opérationnellement défini par uneprolifération cellulaire incontr^olée, impliquant une défaillance del'homeostasie de l'organisme. La recherche sur le cancer vise `a fournir desoutils diagnostics précis et des traitements ajustés pour chacune de cesmaladies. La technologie microarray permet la quantification de l'expression detous les produits de transcription du génome humain et constitue donc un outilpour mieux comprendre la nature polygénique du cancer. La technologiemicroarray permet `a la fois de découvrir de nouvelles classes de cancers et deprédire l'issue de maladie en fonction de profils d'expression préalables. Enoutre, l'utilisation de signatures d'expression géniques en tant que marqueursreprésentatifs de certains processus physiologiques moléculaires permetl'emploi de données microarray pour tester des hypoth`eses biologiques.Cette dissertation a deux objectifs: (a) établir la mesure dans laquelledes marqueurs d'expression génique de la différenciation et de la proliférationcellulaire peuvent contribuer `a la classification des maladies cancéreuses; et(b) d'évaluer l'étendue des signaux pronostiques dans les transcriptomescancéreux.Nous avons mis au point une méthode objective pour extraire des signatures dedifférentiation organe-spécifiques `a partir de données d'expression génique.Nous avons ensuite démontré qu'une signature génique de différentiationtissu-spécifique est capable de distinguer avec précision entre des sous-typeshistologiques de difficile classification dans un mod`ele thyro"idien. Ceci faitpreuve du potentiel valeur clinique et diagnostique des signatures dedifférentiation dans le domaine oncologique.Nous montrons aussi qu'une fraction non négligeable des transcriptomes cancéreuxest capable de prédire l'issue des respectives maladies, `a la suite d'uneanalyse systématique de 114 cohortes de profiles d'expression cancéreuxenglobant 19 types de cancers différents. Cet observation est probablement liée`a une vaste structure de corrélation parmis les profils d'expression cancéreux,partiellement expliquée par des variables techniques et biologiques. Cetteevidence met en cause l'utilisation généralisée d'associations statistiquesentre des marqueurs d'expression géniques et les issues de chaque maladie parmisplusieurs patients afin d'en déduire l'implication de mécanismes biologiquesparticuliers dans la progression du cancer.

Raimondi, Daniele; Gazzo, Andrea; Rooman, Marianne; Lenaerts, Tom; Vranken, Wim

Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects Journal Article

In: Bioinformatics, 32 (12), pp. 1797-1804, 2016, (DOI: 10.1093/bioinformatics/btw094).

Abstract | Links | BibTeX

@article{info:hdl:2013/236692,
title = {Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects},
author = {Daniele Raimondi and Andrea Gazzo and Marianne Rooman and Tom Lenaerts and Wim Vranken},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/236692},
year = {2016},
date = {2016-01-01},
journal = {Bioinformatics},
volume = {32},
number = {12},
pages = {1797-1804},
abstract = {Motivation: There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants. Results: We present DEOGEN, a novel variant effect predictor that can handle both missense SNVs and in-frame INDELs. By integrating information from different biological scales and mimicking the complex mixture of effects that lead from the variant to the phenotype, we obtain significant improvements in the variant-effect prediction results. Next to the typical variant-oriented features based on the evolutionary conservation of the mutated positions, we added a collection of protein-oriented features that are based on functional aspects of the gene affected. We cross-validated DEOGEN on 36 825 polymorphisms, 20 821 deleterious SNVs, and 1038 INDELs from SwissProt. The multilevel contextualization of each (variant, protein) pair in DEOGEN provides a 10% improvement of MCC with respect to current state-of-the-art tools.},
note = {DOI: 10.1093/bioinformatics/btw094},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Ruano, Ana Zafra; Cilia, Elisa; Couceiro, José JR; Sanz, Javier Ruiz; Schymkowitz, Joost J; Rousseau, Frédéric; Luque, Irene; Lenaerts, Tom

From Binding-Induced Dynamic Effects in SH3 Structures to Evolutionary Conserved Sectors. Journal Article

In: PLoS computational biology, 12 (5), pp. e1004938, 2016, (DOI: 10.1371/journal.pcbi.1004938).

Abstract | Links | BibTeX

@article{info:hdl:2013/232621,
title = {From Binding-Induced Dynamic Effects in SH3 Structures to Evolutionary Conserved Sectors.},
author = {Ana Zafra Ruano and Elisa Cilia and José JR Couceiro and Javier Ruiz Sanz and Joost J Schymkowitz and Frédéric Rousseau and Irene Luque and Tom Lenaerts},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/232621/1/PMC4877006.pdf},
year = {2016},
date = {2016-01-01},
journal = {PLoS computational biology},
volume = {12},
number = {5},
pages = {e1004938},
abstract = {Src Homology 3 domains are ubiquitous small interaction modules known to act as docking sites and regulatory elements in a wide range of proteins. Prior experimental NMR work on the SH3 domain of Src showed that ligand binding induces long-range dynamic changes consistent with an induced fit mechanism. The identification of the residues that participate in this mechanism produces a chart that allows for the exploration of the regulatory role of such domains in the activity of the encompassing protein. Here we show that a computational approach focusing on the changes in side chain dynamics through ligand binding identifies equivalent long-range effects in the Src SH3 domain. Mutation of a subset of the predicted residues elicits long-range effects on the binding energetics, emphasizing the relevance of these positions in the definition of intramolecular cooperative networks of signal transduction in this domain. We find further support for this mechanism through the analysis of seven other publically available SH3 domain structures of which the sequences represent diverse SH3 classes. By comparing the eight predictions, we find that, in addition to a dynamic pathway that is relatively conserved throughout all SH3 domains, there are dynamic aspects specific to each domain and homologous subgroups. Our work shows for the first time from a structural perspective, which transduction mechanisms are common between a subset of closely related and distal SH3 domains, while at the same time highlighting the differences in signal transduction that make each family member unique. These results resolve the missing link between structural predictions of dynamic changes and the domain sectors recently identified for SH3 domains through sequence analysis.},
note = {DOI: 10.1371/journal.pcbi.1004938},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Świderska, Ewelina; Łasisz, Jakub; Byrski, Aleksander; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin; Nowe, Ann; Kisiel-Dorohinicki, Marek

Measuring diversity of socio-cognitively inspired ACO search Journal Article

In: Lecture notes in computer science, 9597 , pp. 393-408, 2016, (DOI: 10.1007/978-3-319-31204-0_26).

Abstract | Links | BibTeX

Bugajski, Iwan; Listkiewicz, Piotr; Byrski, Aleksander; Kisiel-Dorohinicki, Marek; Korczynski, Wojciech; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin; Nowe, Ann

Enhancing particle swarm optimization with socio-cognitive inspirations Journal Article

In: Procedia Computer Science, 80 , pp. 804-813, 2016, (DOI: 10.1016/j.procs.2016.05.370).

Abstract | Links | BibTeX

Martinez-Vaquero, Luis L A; Gruji'c, Jelena; Lenaerts, Tom

Equivalence of cooperation indexes: Comment on Universal scaling for the dilemma strength in evolutionary games by Z. Wang et al. Journal Article

In: Physics of life reviews, 16 , pp. 196-197, 2016, (Language of publication: en).

Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

Evolution of commitment and level of participation in public goods games Journal Article

In: Autonomous agents and multi-agent systems, pp. 24, 2016, (DOI: 10.1007/s10458-016-9338-4).

Abstract | Links | BibTeX

Bugajski, Iwan; Byrski, Aleksander; Kisiel-Dorohinicki, Marek; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin

Adaptation of Population Structure in Socio-cognitive Particle Swarm Optimization Journal Article

In: Procedia Computer Science, 101 , pp. 177-186, 2016, (DOI: 10.1016/j.procs.2016.11.022).

Abstract | Links | BibTeX

Cava, Claudia; Colaprico, Antonio; Bertoli, Gloria; Bontempi, Gianluca; Mauri, Giancarlo; Castiglioni, Isabella

How interacting pathways are regulated by miRNAs in breast cancer subtypes Journal Article

In: BMC bioinformatics, 17 , 2016, (DOI: 10.1186/s12859-016-1196-1).

Abstract | Links | BibTeX

Grembergen, Olivier Van; Bizet, Martin; de Bony, Eric James; Calonne, Emilie; Putmans, Pascale; Brohée, Sylvain; Olsen, Catharina; Guo, Mingzhou; Bontempi, Gianluca; Sotiriou, Christos; Defrance, Matthieu; cc, Fran

Portraying breast cancers with long noncoding RNAs Journal Article

In: Science advances, 2 (9), 2016, (DOI: 10.1126/sciadv.1600220).

Abstract | Links | BibTeX

@article{info:hdl:2013/236112,
title = {Portraying breast cancers with long noncoding RNAs},
author = {Olivier Van Grembergen and Martin Bizet and Eric James de Bony and Emilie Calonne and Pascale Putmans and Sylvain Brohée and Catharina Olsen and Mingzhou Guo and Gianluca Bontempi and Christos Sotiriou and Matthieu Defrance and Fran{cc}ois Fuks},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/236112/5/PMC5010371.pdf},
year = {2016},
date = {2016-01-01},
journal = {Science advances},
volume = {2},
number = {9},
abstract = {Evidence is emerging that long noncoding RNAs (lncRNAs) may play a role in cancer development, but this role is not yet clear. We performed a genome-wide transcriptional survey to explore the lncRNA landscape across 995 breast tissue samples. We identified 215 lncRNAs whose genes are aberrantly expressed in breast tumors, as compared to normal samples. Unsupervised hierarchical clustering of breast tumors on the basis of their lncRNAs revealed four breast cancer subgroups that correlate tightly with PAM50-defined mRNA-based subtypes. Using multivariate analysis, we identified no less than 210 lncRNAs prognostic of clinical outcome. By analyzing the coexpression of lncRNA genes and protein-coding genes, we inferred potential functions of the 215 dysregulated lncRNAs. We then associated subtype-specific lncRNAs with key molecular processes involved in cancer. A correlation was observed, on the one hand, between luminal A–specific lncRNAs and the activation of phosphatidylinositol 3-kinase, fibroblast growth factor, and transforming growth factor–β pathways and, on the other hand, between basal-like–specific lncRNAs and the activation of epidermal growth factor receptor (EGFR)–dependent pathways and of the epithelial-to-mesenchymal transition. Finally, we showed that a specific lncRNA, which we called CYTOR, plays a role in breast cancer. We confirmed its predicted functions, showing that it regulates genes involved in the EGFR/mammalian target of rapamycin pathway and is required for cell proliferation, cell migration, and cytoskeleton organization. Overall, our work provides the most comprehensive analyses for lncRNA in breast cancers. Our findings suggest a wide range of biological functions associated with lncRNAs in breast cancer and provide a foundation for functional investigations that could lead to new therapeutic approaches.},
note = {DOI: 10.1126/sciadv.1600220},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Bontempi, Gianluca

A blocking strategy for ranking features according to probabilistic relevance Journal Article

In: Lecture notes in computer science, 10122 LNCS , pp. 59-69, 2016, (DOI: 10.1007/978-3-319-51469-7_5).

Abstract | Links | BibTeX

Silva, Tiago T C; Colaprico, Antonio; Olsen, Catharina; D'Angelo, Fulvio; Bontempi, Gianluca; Ceccarelli, Michele; Noushmehr, Houtan

TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages [version 1; referees: 1 approved, 1 approved with reservations] Journal Article

In: F1000Research, 5 , 2016, (DOI: 10.12688/F1000RESEARCH.8923.1).

Abstract | Links | BibTeX

@article{info:hdl:2013/247761,
title = {TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages [version 1; referees: 1 approved, 1 approved with reservations]},
author = {Tiago T C Silva and Antonio Colaprico and Catharina Olsen and Fulvio D'Angelo and Gianluca Bontempi and Michele Ceccarelli and Houtan Noushmehr},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/247761},
year = {2016},
date = {2016-01-01},
journal = {F1000Research},
volume = {5},
abstract = {Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no comprehensive tool that provides a complete integrative analysis harnessing the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative downstream analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data and by using Roadmap and ENCODE data, we provide a workplan to identify candidate biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAtoolbox, TCGAbiolinks.},
note = {DOI: 10.12688/F1000RESEARCH.8923.1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

"e, Yann-A; Homolova, Adriana; Bontempi, Gianluca

OpenTED browser: Insights into European Public Spendings Journal Article

In: CEUR Workshop Proceedings, 1831 , 2016, (Language of publication: en).

Abstract | Links | BibTeX

Dendievel, Rémi

Sequential stopping under different environments of weak information PhD Thesis

2016, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/239624,
title = {Sequential stopping under different environments of weak information},
author = {Rémi Dendievel},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/239624/5/contratDendievel.pdf},
year = {2016},
date = {2016-01-01},
abstract = {Notre th`ese s’articule autour du th`eme de l’utilisation optimale de l’information contenue dans un mod`ele probabiliste flexible. Dans le premier chapitre, nous couvrons des résultats bien connus des martingales comme le théor`eme de convergence dit L1 des martingales et le théor`eme d’arr^et. Nous discutons de probl`emes ouverts similaires au «last arrival problem» (Bruss et Yor, 2012) qui sont des vrais défis du point de vue théorique et nous ne pouvons que conjecturer la stratégie optimale.Dans les chapitres suivants, nous résolvons des extensions de probl`emes d’arr^et optimal proposés par R. R. Weber (U. Cambridge), basés sur le «théor`eme des odds» (Bruss, 2000). En résumé, il s’agit d’effectuer une seule action (un seul arr^et) lorsque deux suites d’observations indépendantes sont observées simultanément. Nous donnons la solution `a ces probl`emes pour un nombre (fixé) choisi de processus.Le chapitre suivant passe en revue la plupart des développements récents (depuis 2000) réalisés autour du «théor`eme des odds» (Bruss, 2000). Le matériel présenté fut publié (2013), il a donc été mis `a jour dans cette th`ese pour inclure les derniers résultats depuis cette date.Puis nous réservons un chapitre pour une solution explicite pour un cas particulier du Probl`eme d’arr^et optimal de Robbins. Ce chapitre est basé sur un article publié par l’auteur en collaboration avec le professeur Swan (Université de Li`ege). Ce chapitre offre une belle illustration des difficultés rencontrées lorsque trop d’information sur les variables est contenue dans le mod`ele. La solution optimale de ce probl`eme dans le cas général n’est pas connue. Par contre, contre-intuitivement, dans le «last arrival problem» mentionné plus haut, moins d’information permet, comme nous le montrons, de trouver en effet la solution optimale.La th`ese contient un dernier chapitre sur un probl`eme de nature plus combinatoire que nous pouvons lier `a la théorie des graphes dans une certaine mesure. Nous étudions le processus de création d’un graphe aléatoire particulier et les propriétés des cycles créés par celui-ci. Le probl`eme est séquentiel et permet d’envisager des probl`emes d’arr^et intéressants. Cette étude a des conséquences en théorie des graphes, en analyse combinatoire ainsi qu’en science de la chimie combinatoire pour les applications. Un de nos résultats est analogue au résultat de Janson (1987) relatif au premier cycle créé pendant la création de graphes aléatoires.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Notre th`ese s’articule autour du th`eme de l’utilisation optimale de l’information contenue dans un mod`ele probabiliste flexible. Dans le premier chapitre, nous couvrons des résultats bien connus des martingales comme le théor`eme de convergence dit L1 des martingales et le théor`eme d’arr^et. Nous discutons de probl`emes ouverts similaires au «last arrival problem» (Bruss et Yor, 2012) qui sont des vrais défis du point de vue théorique et nous ne pouvons que conjecturer la stratégie optimale.Dans les chapitres suivants, nous résolvons des extensions de probl`emes d’arr^et optimal proposés par R. R. Weber (U. Cambridge), basés sur le «théor`eme des odds» (Bruss, 2000). En résumé, il s’agit d’effectuer une seule action (un seul arr^et) lorsque deux suites d’observations indépendantes sont observées simultanément. Nous donnons la solution `a ces probl`emes pour un nombre (fixé) choisi de processus.Le chapitre suivant passe en revue la plupart des développements récents (depuis 2000) réalisés autour du «théor`eme des odds» (Bruss, 2000). Le matériel présenté fut publié (2013), il a donc été mis `a jour dans cette th`ese pour inclure les derniers résultats depuis cette date.Puis nous réservons un chapitre pour une solution explicite pour un cas particulier du Probl`eme d’arr^et optimal de Robbins. Ce chapitre est basé sur un article publié par l’auteur en collaboration avec le professeur Swan (Université de Li`ege). Ce chapitre offre une belle illustration des difficultés rencontrées lorsque trop d’information sur les variables est contenue dans le mod`ele. La solution optimale de ce probl`eme dans le cas général n’est pas connue. Par contre, contre-intuitivement, dans le «last arrival problem» mentionné plus haut, moins d’information permet, comme nous le montrons, de trouver en effet la solution optimale.La th`ese contient un dernier chapitre sur un probl`eme de nature plus combinatoire que nous pouvons lier `a la théorie des graphes dans une certaine mesure. Nous étudions le processus de création d’un graphe aléatoire particulier et les propriétés des cycles créés par celui-ci. Le probl`eme est séquentiel et permet d’envisager des probl`emes d’arr^et intéressants. Cette étude a des conséquences en théorie des graphes, en analyse combinatoire ainsi qu’en science de la chimie combinatoire pour les applications. Un de nos résultats est analogue au résultat de Janson (1987) relatif au premier cycle créé pendant la création de graphes aléatoires.

Zisis, Ioannis

The Effect of Group Formation on Behaviour: An Experimental and Evolutionary Analysis PhD Thesis

2016, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/231974b,
title = {The Effect of Group Formation on Behaviour: An Experimental and Evolutionary Analysis},
author = {Ioannis Zisis},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/231974/5/contratZisis.pdf},
year = {2016},
date = {2016-01-01},
abstract = {The division of resources between a group of people may cause con- flicts: Individuals with varying roles and responsibilities will claim different shares of the surplus to be divided. In this dissertation, we analyze how the decision to form a group will influence the bargaining behaviour of the members of that group. People will act collectively as certain tasks may require the participation of a specific number of individuals before it can be completed. We examine whether certain mechanisms can efficiently promote group formation for the sake of surplus production, and then, what will be the effect of these mechanisms on the behaviour of the group members. For these reasons, we constructed a novel surplus production and distribution interaction which we call the Anticipation Game (AG). The AG can be played between only two players (pairwise interaction) or among more then two players (group interaction). In our study we will analyze both the pairwise AG and the group version of AG, first by obtaining our own empirical data and then by performing a stochastic evolutionary analysis. We aim to provide answers on: i) how will a reputation based partner approval mechanism influence the surplus distribution in both the pairwise and the group AG, ii) will then limitations in obtaining the reputation of a potential partner alter the results of the pairwise AG?, iii) will we notice any effect on the behaviour of players when they can repeatedly cooperate with the same partners in group interactions, iv) how natural selection may have shaped the behaviour of players in group formation interactions (both pairwise and group AG evolutionary analysis).},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Lenaerts, Tom

Conditions for the evolution of apology and forgiveness in populations of autonomous agents Inproceedings

In: The 2016 AAAI Spring Symposium Series: Technical Reports, 2016, (Conference: AAAI Spring Symposium on “Ethical and Moral Considerations in Non-Human Agents”.(Stanford, USA)).

Links | BibTeX

Pereira, Luís Moniz; Han, The Anh T A H; Martinez-Vaquero, Luis L A; Lenaerts, Tom

Guilt for non-humans Inproceedings

In: The 2016 AAAI Spring Symposium Series: Technical reports, 2016, (Conference: The 2016 AAAI Spring Symposium on “Ethical and Moral Considerations in Non-Human Agents”(21-23 March 2016: Stanford, USA)).

Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

Emergence of Cooperation in Group Interactions: Avoidance versus Restriction Inproceedings

In: The 2016 AAAI Spring Symposium Series: Technical Reports, 2016, (Conference: AAAI Spring Symposium on “Ethical and Moral Considerations in Non-Human Agents”(21-23 March 2016: Stanford, USA)).

Links | BibTeX

Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom; Zisis, Ioannis

Endogenous Repeated Cooperation and Surplus Distribution - An Experimental Analysis Technical Report

2016, (Language of publication: en).

Abstract | Links | BibTeX

@techreport{info:hdl:2013/228058,
title = {Endogenous Repeated Cooperation and Surplus Distribution - An Experimental Analysis},
author = {Sibilla Di Guida and The Anh T A H Han and Georg Kirchsteiger and Tom Lenaerts and Ioannis Zisis},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/228058/3/2016-08-DIGUIDA_HAN_KIRCHSTEIGER_LENAERTS_ZISIS-endog.pdf},
year = {2016},
date = {2016-01-01},
abstract = {This paper investigates experimentally how the endogenous group formation combined with the possibility of repeated interaction impacts cooperation levels and surplus distribution. We developed a Surplus Production Distribution Game where the cooperation of four agents is needed to produce a surplus. In case of cooperation, two of the four subjects, the distributors, decided how much of surplus each of them wanted to give to the two other agents, the receivers. This game was played repeatedly with different matching procedures. In the Re-match Treatment (RT) the subjects got randomly re-matched every round, while in the Endogenous-match Treatment (ET) a group was maintained as long as its members cooperated. There was also a Base treatment (BT) where cooperation was exogenously enforced. We found that the distributor's contributions were higher in the ET and the RT than in the BT - unsurprisingly, receivers' possibility to refuse cooperation led to more equal surplus distributions. But contrary to commonly hold beliefs, the possibility of repeated interaction did not lead to higher cooperation levels and more equal allocations of the surplus. Instead, endogenous group formation combined with the possibility of repeated interaction led to self-selection of the subjects in the ET. The endogenous group duration varied drastically between different groups in the ET, with long-lived groups exhibiting contributions and cooperation levels higher than in the RT, while short-lived groups showed contributions and cooperation levels lower than in the RT. Furthermore, for given contribution levels, receivers were more likely to refuse cooperation when their average relationship length was short. This shows that long-lived groups consisted of generous distributors and not so demanding receivers, while ungenerous distributors and demanding receivers formed short-lived groups. Hence, the possibility of repeated interaction does not necessarily increase cooperation and efficiency levels when combined with endogenous group formation. Rather, such a situation might lead to self-selection of agents.},
note = {Language of publication: en},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}

This paper investigates experimentally how the endogenous group formation combined with the possibility of repeated interaction impacts cooperation levels and surplus distribution. We developed a Surplus Production Distribution Game where the cooperation of four agents is needed to produce a surplus. In case of cooperation, two of the four subjects, the distributors, decided how much of surplus each of them wanted to give to the two other agents, the receivers. This game was played repeatedly with different matching procedures. In the Re-match Treatment (RT) the subjects got randomly re-matched every round, while in the Endogenous-match Treatment (ET) a group was maintained as long as its members cooperated. There was also a Base treatment (BT) where cooperation was exogenously enforced. We found that the distributor's contributions were higher in the ET and the RT than in the BT - unsurprisingly, receivers' possibility to refuse cooperation led to more equal surplus distributions. But contrary to commonly hold beliefs, the possibility of repeated interaction did not lead to higher cooperation levels and more equal allocations of the surplus. Instead, endogenous group formation combined with the possibility of repeated interaction led to self-selection of the subjects in the ET. The endogenous group duration varied drastically between different groups in the ET, with long-lived groups exhibiting contributions and cooperation levels higher than in the RT, while short-lived groups showed contributions and cooperation levels lower than in the RT. Furthermore, for given contribution levels, receivers were more likely to refuse cooperation when their average relationship length was short. This shows that long-lived groups consisted of generous distributors and not so demanding receivers, while ungenerous distributors and demanding receivers formed short-lived groups. Hence, the possibility of repeated interaction does not necessarily increase cooperation and efficiency levels when combined with endogenous group formation. Rather, such a situation might lead to self-selection of agents.

Lenaerts, Tom; Gazzo, Andrea; Daneels, Dorien; Cilia, Elisa; Bonduelle, Maryse; Abramowicz, Marc; Dooren, Sonia Van; Smits, Guillaume

Exploring the variant combinations in the digenic diseases database DIDA Miscellaneous

2016, (Conference: uropean Conference on Computational Biology(3-7 September 2016: Den Hague, the Netherlands)).

Links | BibTeX

Martinez-Vaquero, Luis L A; Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

Apology and forgiveness evolve to resolve failures in cooperative agreements Miscellaneous

2016, (Conference: Benelux Conference on Artificial Intelligence(10-11 November 2016: Amsterdam. the Netherlands)).

Links | BibTeX

Raimondi, Daniele; Gazzo, Andrea; Rooman, Marianne; Lenaerts, Tom; Vranken, Wim

Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects Miscellaneous

2016, (Conference: European Conference on Computational Biology(3-7 September 2016: Den Hague, the Netherlands)).

Links | BibTeX

Gazzo, Andrea; Raimondi, Daniele; Daneels, Dorien; Smits, Guillaume; Dooren, Sonia Van; Lenaerts, Tom

Predicting oligogenic effects using digenic disease data Miscellaneous

2016, (Conference: European Conference on Computational Biology(3-7 September 2016: Den Hague, the Netherlands)).

Links | BibTeX

Orlando, Gabriele; Raimondi, Daniele; Lenaerts, Tom; Vranken, Wim F

Rigapollo, a HMM-SVM based approach to sequence alignment Miscellaneous

2016, (Conference: European Conference on Computational Biology(3-7 September 2016: Den Hague, the Netherlands)).

Links | BibTeX

Martinez-Vaquero, Luis L A; Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

Forgiveness evolves to ensure cooperation in long-term agreements Miscellaneous

2016, (Conference: Conference on Complex Systems(19-22 September 2016: Amsterdam, the Netherlands)).

Links | BibTeX

Papadimitriou, Sofia; Gazzo, Andrea; Smits, Guillaume; Nowe, Ann; Lenaerts, Tom

Predicting digenic variant effects with DIDA Miscellaneous

2016, (Conference: European Conference on Computational Biology(3-7 September 2016: Den Hague, the Netherlands)).

Links | BibTeX

Wit, Nathalie De; Lenaerts, Tom

Cluster analysis of neurodevelopmental diseases with Spark. Informatique Masters Thesis

2016, (Language of publication: fr).

Links | BibTeX

Steckelmacher, Denis; Lenaerts, Tom

Reinforcement learning in complex environments: Evaluating algorithms on image classification. Informatique Masters Thesis

2016, (Language of publication: fr).

Links | BibTeX

Velde, Thibaut Van De; Lenaerts, Tom

Concevoir un mod`ele de jeu `a ressources communes au sein d'un cloud. Informatique Masters Thesis

2016, (Language of publication: fr).

Links | BibTeX

Zisis, Ioannis

The Effect of Group Formation on Behaviour: An Experimental and Evolutionary Analysis PhD Thesis

2016, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/231974,
title = {The Effect of Group Formation on Behaviour: An Experimental and Evolutionary Analysis},
author = {Ioannis Zisis},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/231974/5/contratZisis.pdf},
year = {2016},
date = {2016-01-01},
abstract = {The division of resources between a group of people may cause con- flicts: Individuals with varying roles and responsibilities will claim different shares of the surplus to be divided. In this dissertation, we analyze how the decision to form a group will influence the bargaining behaviour of the members of that group. People will act collectively as certain tasks may require the participation of a specific number of individuals before it can be completed. We examine whether certain mechanisms can efficiently promote group formation for the sake of surplus production, and then, what will be the effect of these mechanisms on the behaviour of the group members. For these reasons, we constructed a novel surplus production and distribution interaction which we call the Anticipation Game (AG). The AG can be played between only two players (pairwise interaction) or among more then two players (group interaction). In our study we will analyze both the pairwise AG and the group version of AG, first by obtaining our own empirical data and then by performing a stochastic evolutionary analysis. We aim to provide answers on: i) how will a reputation based partner approval mechanism influence the surplus distribution in both the pairwise and the group AG, ii) will then limitations in obtaining the reputation of a potential partner alter the results of the pairwise AG?, iii) will we notice any effect on the behaviour of players when they can repeatedly cooperate with the same partners in group interactions, iv) how natural selection may have shaped the behaviour of players in group formation interactions (both pairwise and group AG evolutionary analysis).},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Tarabichi, Maxime

Integrative analyses of genome-wide transcriptomic and genomic thyroid cancer profiles PhD Thesis

2016, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/225138,
title = {Integrative analyses of genome-wide transcriptomic and genomic thyroid cancer profiles},
author = {Maxime Tarabichi},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/225138/5/ContratMaximeTarabichi.pdf},
year = {2016},
date = {2016-01-01},
abstract = {Cette th`ese en bioinformatique a été réalisée entre 2010 et 2015 dans le groupe du Pr. Vincent Detours `a l’Institut de Recherche Interdisciplinaire en Biologie Humaine et Moléculaire. Nous avons analysé des données génomiques et transcriptomiques provenant de carcinomes papillaires de la thyro"ide (CPTs) et leurs tissus non-cancéreux adjacents. La premi`ere partie étudiait les différences transcriptomiques entre CPTs post-Tchernobyl et CPTs sporadiques, et leur tissus non-cancéreux adjacents. Dans notre cohorte, les cas sporadiques étaient en moyenne et significativement un an plus jeunes. Apr`es un ajustement des données transcriptionnelles pour l'^age, pr`es de 400 g`enes étaient plus exprimés dans les tissus adjacents des patients exposés aux radiations. Cependant, nous n’avons pu détecter aucune surreprésentation de groupe de g`enes participant `a des fonctions biologiques connues. Il était possible de distinguer les cas sporadiques des cas post-Tchernobyl sur base des transcriptomes de leurs tissus adjacents, avec une précision de ~70%. Cette surexpression de g`enes dans les tissus non-cancéreux adjacents pourrait ^etre liée `a une radiosensibilité accrue dans le groupe des patients exposés aux radiations de Tchernobyl. Dans la deuxi`eme étude, nous avons intégré des données provenant des patients de la premi`ere partie, incluant les nombres de copies d'ADN des CPTs, le génotype de plus de 400.000 SNPs dans le sang et les données transcriptionnelles des CPTs et leurs tissus non-cancéreux adjacents. En reproduisant les résultats d'une étude précédente, nous avons retrouvé la région 7q11.23 dupliquée exclusivement dans un tiers des patients exposés aux radiations. Dans une étude indépendante, un autre groupe a montré que la duplication de cette région était plus fréquente dans une population de lignées cellulaires radiosensibles que dans la population humaine normale. Cependant, en analysant les transcriptomes des patients présentant cette duplication, nous n'avons pas détecté de différence d’expression des g`enes codés dans cette région génomique. En outre, aucun génotype de SNP n'était significativement lié `a l'exposition aux radiations. En conclusion, les résultats confirment qu'un tiers des CPTs post-Tchernobyl ont des traces d'un dég^at radio-sensibilsant dans leur ADN. Dans une troisi`eme étude, nous avons étudié les différences transcriptionnelles entre CPTs et leurs métastases ganglionnaires (MGs) associées, ainsi qu'entre des CPTs développant des MGs (N+) et des CPTs ne développant pas de MGs (N0). Des études précédentes comparant les MGs et leurs tumeurs associées impliquant d’autres organes ont montré une surexpression de g`enes dans les MGs, liés aux cellules immunitaires. Ce signal provient du tissu contaminant environnant les MGs. Pour se défaire de ce signal contaminant, d’autres études ont microdisséqué au laser les parties tumorales des MGs. Cependant, la microdissection retire aussi le stroma associé `a la tumeur, alors que celui-ci est justement impliqué dans la progression tumorale. Gr^ace `a une méthode originale, nous avons corrigé nos données d’expression des MGs pour leur contenu en contaminant ganglionnaire non-cancéreux. Apr`es cette correction, l’expression de g`enes liés au stroma était plus élevée dans les MGs que dans leurs CPTs. Les différences d’expression entre N0 et N+ n’étaient pas reproductibles entre 4 jeux de données indépendants de CPTs. Ceci démontre l’absence d’un signal transcriptionnelle lié au statut nodal dans ces données. Cependant, en utilisant des données publiques comprenant des centaines de tumeurs, il est possible de prédire le statut nodal (N0 ou N+) des CPTs ainsi que des cancers du sein et du colon `a partir de leurs transcriptomes. Des études précédentes montraient des taux de prédiction presque parfaits (>90%) du statut nodal `a partir des données transcriptomiques. Nous avons décelés dans ces études le m^eme biais technique de sélection des g`enes, qui peut expliquer ces taux artificiellement élevés. Dans notre étude, ce biais n’était pas présent et la précision de nos prédictions était limitée (<70%), questionnant l’intér^et clinique de telles prédictions. La présence d’un signal permettant de prédire le statut nodal et l’irreproductibilité de ce signal dans des jeux de données indépendants peuvent s'expliquer par l’association entre le statut nodal et des caractéristiques d'agressivité des tumeurs, qui pourraient, elles, avoir une influence reproductible sur les transcriptomes. Dans notre derni`ere étude, nous avons analysé les différences entre CPTs, liées `a la présence de BRAFV600E, une mutation commune `a 60% des CPTs. En utilisant un jeu de données public, nous avons montré que les CPTs présentant la mutation étaient plus dédifférenciés, et plus infiltrés en stroma, probablement en lymphocytes et fibroblastes; et que ces CPTs présentaient plus de fibrose et proliféraient sans doute plus. Tout ceci sugg`ere que les CPTs mutés pour BRAF constituent un groupe de CPTs plus agressif. Des caractéristiques d’agressivité pourraient ^etre détectées au front invasif, c’est-`a-dire la périphérie de la tumeur définissant son contact avec le stroma, notamment la présence de regroupement de cellules isolées du reste de la tumeur. Dans les CPTs, ces ^ilots cellulaires isolés sont observés sur des lames histologiques 2D et pourraient ^etre expliqués soit par un détachement cellulaire, signe d’agressivité lié au processus métastatique, soit une conformation complexe compatible avec une tumeur connexe en 3D. Dans un CPT, nous avons analysé la conformation 3D du front invasif d'un CPT muté. Nous avons reconstruit son volume 3D gr^ace `a une méthode originale. Les groupes de cellules cancéreuses qui semblaient isolées sur les images 2D d’histopathologie, étaient en fait connectés en 3D. L’hypoth`ese de la présence de détachement cellulaire suite `a la transition épithélio-mésenchymateuse n’est donc pas requise pour expliquer la présence de ces ^ilots cellulaires en 2D. La forme 3D du front invasif impliquait une surface de contact entre tumeur et stroma bien plus importante qu'impliquée par la forme ellipso"ide habituellement décrite. Les fibroblastes participaient autant `a la création de la masse tumorale que les cellules cancéreuses, puisque ces deux groupes de cellules proliféraient `a la m^eme vitesse. A l'avenir, le séquenccage du matériel génétique de cellules individuelles facilitera notre interprétation des signaux génomiques et transcriptomiques, qui jusqu’alors provenaient de tissu complet, i.e. un mélange de populations de cellules tumorales, stromales et de contaminant. Une signature de radiation pourrait ^etre extraite des profils mutationnels de cellules individuelles exposées aux radiations et `a l’H2O2 in vitro et comparée `a la signature des CTPs post-Tchernobyl. Les cellules tumorales et stromales individuelles des MGs pourraient ^etre comparées aux cellules tumorales et stromales invividuelles des CPTs. De m^eme les cellules individuelles mutées pour BRAFV600E pourraient ^etre comparées aux cellules non mutées.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Cette th`ese en bioinformatique a été réalisée entre 2010 et 2015 dans le groupe du Pr. Vincent Detours `a l’Institut de Recherche Interdisciplinaire en Biologie Humaine et Moléculaire. Nous avons analysé des données génomiques et transcriptomiques provenant de carcinomes papillaires de la thyro"ide (CPTs) et leurs tissus non-cancéreux adjacents. La premi`ere partie étudiait les différences transcriptomiques entre CPTs post-Tchernobyl et CPTs sporadiques, et leur tissus non-cancéreux adjacents. Dans notre cohorte, les cas sporadiques étaient en moyenne et significativement un an plus jeunes. Apr`es un ajustement des données transcriptionnelles pour l'^age, pr`es de 400 g`enes étaient plus exprimés dans les tissus adjacents des patients exposés aux radiations. Cependant, nous n’avons pu détecter aucune surreprésentation de groupe de g`enes participant `a des fonctions biologiques connues. Il était possible de distinguer les cas sporadiques des cas post-Tchernobyl sur base des transcriptomes de leurs tissus adjacents, avec une précision de ~70%. Cette surexpression de g`enes dans les tissus non-cancéreux adjacents pourrait ^etre liée `a une radiosensibilité accrue dans le groupe des patients exposés aux radiations de Tchernobyl. Dans la deuxi`eme étude, nous avons intégré des données provenant des patients de la premi`ere partie, incluant les nombres de copies d'ADN des CPTs, le génotype de plus de 400.000 SNPs dans le sang et les données transcriptionnelles des CPTs et leurs tissus non-cancéreux adjacents. En reproduisant les résultats d'une étude précédente, nous avons retrouvé la région 7q11.23 dupliquée exclusivement dans un tiers des patients exposés aux radiations. Dans une étude indépendante, un autre groupe a montré que la duplication de cette région était plus fréquente dans une population de lignées cellulaires radiosensibles que dans la population humaine normale. Cependant, en analysant les transcriptomes des patients présentant cette duplication, nous n'avons pas détecté de différence d’expression des g`enes codés dans cette région génomique. En outre, aucun génotype de SNP n'était significativement lié `a l'exposition aux radiations. En conclusion, les résultats confirment qu'un tiers des CPTs post-Tchernobyl ont des traces d'un dég^at radio-sensibilsant dans leur ADN. Dans une troisi`eme étude, nous avons étudié les différences transcriptionnelles entre CPTs et leurs métastases ganglionnaires (MGs) associées, ainsi qu'entre des CPTs développant des MGs (N+) et des CPTs ne développant pas de MGs (N0). Des études précédentes comparant les MGs et leurs tumeurs associées impliquant d’autres organes ont montré une surexpression de g`enes dans les MGs, liés aux cellules immunitaires. Ce signal provient du tissu contaminant environnant les MGs. Pour se défaire de ce signal contaminant, d’autres études ont microdisséqué au laser les parties tumorales des MGs. Cependant, la microdissection retire aussi le stroma associé `a la tumeur, alors que celui-ci est justement impliqué dans la progression tumorale. Gr^ace `a une méthode originale, nous avons corrigé nos données d’expression des MGs pour leur contenu en contaminant ganglionnaire non-cancéreux. Apr`es cette correction, l’expression de g`enes liés au stroma était plus élevée dans les MGs que dans leurs CPTs. Les différences d’expression entre N0 et N+ n’étaient pas reproductibles entre 4 jeux de données indépendants de CPTs. Ceci démontre l’absence d’un signal transcriptionnelle lié au statut nodal dans ces données. Cependant, en utilisant des données publiques comprenant des centaines de tumeurs, il est possible de prédire le statut nodal (N0 ou N+) des CPTs ainsi que des cancers du sein et du colon `a partir de leurs transcriptomes. Des études précédentes montraient des taux de prédiction presque parfaits (>90%) du statut nodal `a partir des données transcriptomiques. Nous avons décelés dans ces études le m^eme biais technique de sélection des g`enes, qui peut expliquer ces taux artificiellement élevés. Dans notre étude, ce biais n’était pas présent et la précision de nos prédictions était limitée (<70%), questionnant l’intér^et clinique de telles prédictions. La présence d’un signal permettant de prédire le statut nodal et l’irreproductibilité de ce signal dans des jeux de données indépendants peuvent s'expliquer par l’association entre le statut nodal et des caractéristiques d'agressivité des tumeurs, qui pourraient, elles, avoir une influence reproductible sur les transcriptomes. Dans notre derni`ere étude, nous avons analysé les différences entre CPTs, liées `a la présence de BRAFV600E, une mutation commune `a 60% des CPTs. En utilisant un jeu de données public, nous avons montré que les CPTs présentant la mutation étaient plus dédifférenciés, et plus infiltrés en stroma, probablement en lymphocytes et fibroblastes; et que ces CPTs présentaient plus de fibrose et proliféraient sans doute plus. Tout ceci sugg`ere que les CPTs mutés pour BRAF constituent un groupe de CPTs plus agressif. Des caractéristiques d’agressivité pourraient ^etre détectées au front invasif, c’est-`a-dire la périphérie de la tumeur définissant son contact avec le stroma, notamment la présence de regroupement de cellules isolées du reste de la tumeur. Dans les CPTs, ces ^ilots cellulaires isolés sont observés sur des lames histologiques 2D et pourraient ^etre expliqués soit par un détachement cellulaire, signe d’agressivité lié au processus métastatique, soit une conformation complexe compatible avec une tumeur connexe en 3D. Dans un CPT, nous avons analysé la conformation 3D du front invasif d'un CPT muté. Nous avons reconstruit son volume 3D gr^ace `a une méthode originale. Les groupes de cellules cancéreuses qui semblaient isolées sur les images 2D d’histopathologie, étaient en fait connectés en 3D. L’hypoth`ese de la présence de détachement cellulaire suite `a la transition épithélio-mésenchymateuse n’est donc pas requise pour expliquer la présence de ces ^ilots cellulaires en 2D. La forme 3D du front invasif impliquait une surface de contact entre tumeur et stroma bien plus importante qu'impliquée par la forme ellipso"ide habituellement décrite. Les fibroblastes participaient autant `a la création de la masse tumorale que les cellules cancéreuses, puisque ces deux groupes de cellules proliféraient `a la m^eme vitesse. A l'avenir, le séquenccage du matériel génétique de cellules individuelles facilitera notre interprétation des signaux génomiques et transcriptomiques, qui jusqu’alors provenaient de tissu complet, i.e. un mélange de populations de cellules tumorales, stromales et de contaminant. Une signature de radiation pourrait ^etre extraite des profils mutationnels de cellules individuelles exposées aux radiations et `a l’H2O2 in vitro et comparée `a la signature des CTPs post-Tchernobyl. Les cellules tumorales et stromales individuelles des MGs pourraient ^etre comparées aux cellules tumorales et stromales invividuelles des CPTs. De m^eme les cellules individuelles mutées pour BRAFV600E pourraient ^etre comparées aux cellules non mutées.

2015

Lopes, Miguel

Inference of gene networks from time series expression data and application to type 1 Diabetes PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/216729,
title = {Inference of gene networks from time series expression data and application to type 1 Diabetes},
author = {Miguel Lopes},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/216729/6/contratGasparLopes.pdf},
year = {2015},
date = {2015-01-01},
abstract = {The inference of gene regulatory networks (GRN) is of great importance to medical research, as causal mechanisms responsible for phenotypes are unravelled and potential therapeutical targets identified. In type 1 diabetes, insulin producing pancreatic beta-cells are the target of an auto-immune attack leading to apoptosis (cell suicide). Although key genes and regulations have been identified, a precise characterization of the process leading to beta-cell apoptosis has not been achieved yet. The inference of relevant molecular pathways in type 1 diabetes is then a crucial research topic. GRN inference from gene expression data (obtained from microarrays and RNA-seq technology) is a causal inference problem which may be tackled with well-established statistical and machine learning concepts. In particular, the use of time series facilitates the identification of the causal direction in cause-effect gene pairs. However, inference from gene expression data is a very challenging problem due to the large number of existing genes (in human, over twenty thousand) and the typical low number of samples in gene expression datasets. In this context, it is important to correctly assess the accuracy of network inference methods. The contributions of this thesis are on three distinct aspects. The first is on inference assessment using precision-recall curves, in particular using the area under the curve (AUPRC). The typical approach to assess AUPRC significance is using Monte Carlo, and a parametric alternative is proposed. It consists on deriving the mean and variance of the null AUPRC and then using these parameters to fit a beta distribution approximating the true distribution. The second contribution is an investigation on network inference from time series. Several state of the art strategies are experimentally assessed and novel heuristics are proposed. One is a fast approximation of first order Granger causality scores, suited for GRN inference in the large variable case. Another identifies co-regulated genes (ie. regulated by the same genes). Both are experimentally validated using microarray and simulated time series. The third contribution of this thesis is on the context of type 1 diabetes and is a study on beta cell gene expression after exposure to cytokines, emulating the mechanisms leading to apoptosis. 8 datasets of beta cell gene expression were used to identify differentially expressed genes before and after 24h, which were functionally characterized using bioinformatics tools. The two most differentially expressed genes, previously unknown in the type 1 Diabetes literature (RIPK2 and ELF3) were found to modulate cytokine induced apoptosis. A regulatory network was then inferred using a dynamic adaptation of a state of the art network inference method. Three out of four predicted regulations (involving RIPK2 and ELF3) were experimentally confirmed, providing a proof of concept for the adopted approach.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

The inference of gene regulatory networks (GRN) is of great importance to medical research, as causal mechanisms responsible for phenotypes are unravelled and potential therapeutical targets identified. In type 1 diabetes, insulin producing pancreatic beta-cells are the target of an auto-immune attack leading to apoptosis (cell suicide). Although key genes and regulations have been identified, a precise characterization of the process leading to beta-cell apoptosis has not been achieved yet. The inference of relevant molecular pathways in type 1 diabetes is then a crucial research topic. GRN inference from gene expression data (obtained from microarrays and RNA-seq technology) is a causal inference problem which may be tackled with well-established statistical and machine learning concepts. In particular, the use of time series facilitates the identification of the causal direction in cause-effect gene pairs. However, inference from gene expression data is a very challenging problem due to the large number of existing genes (in human, over twenty thousand) and the typical low number of samples in gene expression datasets. In this context, it is important to correctly assess the accuracy of network inference methods. The contributions of this thesis are on three distinct aspects. The first is on inference assessment using precision-recall curves, in particular using the area under the curve (AUPRC). The typical approach to assess AUPRC significance is using Monte Carlo, and a parametric alternative is proposed. It consists on deriving the mean and variance of the null AUPRC and then using these parameters to fit a beta distribution approximating the true distribution. The second contribution is an investigation on network inference from time series. Several state of the art strategies are experimentally assessed and novel heuristics are proposed. One is a fast approximation of first order Granger causality scores, suited for GRN inference in the large variable case. Another identifies co-regulated genes (ie. regulated by the same genes). Both are experimentally validated using microarray and simulated time series. The third contribution of this thesis is on the context of type 1 diabetes and is a study on beta cell gene expression after exposure to cytokines, emulating the mechanisms leading to apoptosis. 8 datasets of beta cell gene expression were used to identify differentially expressed genes before and after 24h, which were functionally characterized using bioinformatics tools. The two most differentially expressed genes, previously unknown in the type 1 Diabetes literature (RIPK2 and ELF3) were found to modulate cytokine induced apoptosis. A regulatory network was then inferred using a dynamic adaptation of a state of the art network inference method. Three out of four predicted regulations (involving RIPK2 and ELF3) were experimentally confirmed, providing a proof of concept for the adopted approach.

Hajingabo, Leon

Analyzing molecular network perturbations in human cancer: application to mutated genes and gene fusions involved in acute lymphoblastic leukemia PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209126,
title = {Analyzing molecular network perturbations in human cancer: application to mutated genes and gene fusions involved in acute lymphoblastic leukemia},
author = {Leon Hajingabo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209126/3/d6d6225e-dfa8-46a3-985a-2ef19c71eff1.txt},
year = {2015},
date = {2015-01-01},
abstract = {Le séquenccage du génome humain et l'émergence de nouvelles technologies de génomique `a haut débit, ont initié de nouveaux mod`eles d'investigation pour l'analyse systématique des maladies humaines. Actuellement, nous pouvons tenter de comprendre les maladies tel que le cancer avec une perspective plus globale, en identifiant des g`enes responsables des cancers et en étudiant la mani`ere dont leurs produits protéiques fonctionnent dans un réseau d’interactions moléculaires. Dans ce contexte, nous avons collecté les g`enes spécifiquement liés `a la Leucémie Lymphoblastique Aigu"e (LLA), et identifié de nouveaux partenaires d'interaction qui relient ces g`enes clés associés `a la LLA tels que NOTCH1, FBW7, KRAS et PTPN11, dans un réseau d’interactions. Nous avons également tenté de prédire l’impact fonctionnel des variations génomiques tel que des fusions de g`enes impliquées dans LLA. En utilisant comme mod`eles trois différentes translocations chromosomiques ETV6-RUNX1 (TEL-AML1), BCR-ABL1, et E2A-PBX1 (TCF3-PBX1) fréquemment identifiées dans des cellules B LLA, nous avons adapté une approche de prédiction d’oncog`enes afin de prédire des perturbations moléculaires dans la LLA. Nous avons montré que les circuits transcriptomiques dépendant de Myc et JunD sont spécifiquement dérégulés suite aux fusions de g`enes TEL-AML1 et TCF3-PBX1, respectivement. Nous avons également identifié le mécanisme de transport des ARNm dépendant du facteur NXF1 comme une cible directe de la protéine de fusion TCF3-PBX1. Gr^ace `a cette approche combinant les données interactomiques et les analyses d'expression génique, nous avons fourni un nouvel aperccu `a la compréhension moléculaire de la Leucémie Lymphoblastique Aigu"e.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Pozzolo, Andrea Dal; Caelen, Olivier; Bontempi, Gianluca

When is undersampling effective in unbalanced classification tasks? Book Chapter

In: Springer, 2015, (Language of publication: en).

Links | BibTeX

Pozzolo, Andrea Dal

Adaptive Machine Learning for Credit Card Fraud Detection PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

Lopes, Miguel

Inference of gene networks from time series expression data and application to type 1 Diabetes PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/216729b,
title = {Inference of gene networks from time series expression data and application to type 1 Diabetes},
author = {Miguel Lopes},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/216729/6/contratGasparLopes.pdf},
year = {2015},
date = {2015-01-01},
abstract = {The inference of gene regulatory networks (GRN) is of great importance to medical research, as causal mechanisms responsible for phenotypes are unravelled and potential therapeutical targets identified. In type 1 diabetes, insulin producing pancreatic beta-cells are the target of an auto-immune attack leading to apoptosis (cell suicide). Although key genes and regulations have been identified, a precise characterization of the process leading to beta-cell apoptosis has not been achieved yet. The inference of relevant molecular pathways in type 1 diabetes is then a crucial research topic. GRN inference from gene expression data (obtained from microarrays and RNA-seq technology) is a causal inference problem which may be tackled with well-established statistical and machine learning concepts. In particular, the use of time series facilitates the identification of the causal direction in cause-effect gene pairs. However, inference from gene expression data is a very challenging problem due to the large number of existing genes (in human, over twenty thousand) and the typical low number of samples in gene expression datasets. In this context, it is important to correctly assess the accuracy of network inference methods. The contributions of this thesis are on three distinct aspects. The first is on inference assessment using precision-recall curves, in particular using the area under the curve (AUPRC). The typical approach to assess AUPRC significance is using Monte Carlo, and a parametric alternative is proposed. It consists on deriving the mean and variance of the null AUPRC and then using these parameters to fit a beta distribution approximating the true distribution. The second contribution is an investigation on network inference from time series. Several state of the art strategies are experimentally assessed and novel heuristics are proposed. One is a fast approximation of first order Granger causality scores, suited for GRN inference in the large variable case. Another identifies co-regulated genes (ie. regulated by the same genes). Both are experimentally validated using microarray and simulated time series. The third contribution of this thesis is on the context of type 1 diabetes and is a study on beta cell gene expression after exposure to cytokines, emulating the mechanisms leading to apoptosis. 8 datasets of beta cell gene expression were used to identify differentially expressed genes before and after 24h, which were functionally characterized using bioinformatics tools. The two most differentially expressed genes, previously unknown in the type 1 Diabetes literature (RIPK2 and ELF3) were found to modulate cytokine induced apoptosis. A regulatory network was then inferred using a dynamic adaptation of a state of the art network inference method. Three out of four predicted regulations (involving RIPK2 and ELF3) were experimentally confirmed, providing a proof of concept for the adopted approach.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Lerman, Liran

A machine learning approach for automatic and generic side-channel attacks PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209070,
title = {A machine learning approach for automatic and generic side-channel attacks},
author = {Liran Lerman},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209070/2/be487c5b-7b94-414c-bf2e-96847aa98284.txt},
year = {2015},
date = {2015-01-01},
abstract = {L'omniprésence de dispositifs interconnectés am`ene `a un intér^et massif pour la sécurité informatique fournie entre autres par le domaine de la cryptographie. Pendant des décennies, les spécialistes en cryptographie estimaient le niveau de sécurité d'un algorithme cryptographique indépendamment de son implantation dans un dispositif. Cependant, depuis la publication des attaques d'implantation en 1996, les attaques physiques sont devenues un domaine de recherche actif en considérant les propriétés physiques de dispositifs cryptographiques. Dans notre dissertation, nous nous concentrons sur les attaques profilées. Traditionnellement, les attaques profilées appliquent des méthodes paramétriques dans lesquelles une information a priori sur les propriétés physiques est supposée. Le domaine de l'apprentissage automatique produit des mod`eles automatiques et génériques ne nécessitant pas une information a priori sur le phénom`ene étudié.

Cette dissertation apporte un éclairage nouveau sur les capacités des méthodes d'apprentissage automatique. Nous démontrons d'abord que les attaques profilées paramétriques surpassent les méthodes d'apprentissage automatique lorsqu'il n'y a pas d'erreur d'estimation ni d'hypoth`ese. En revanche, les attaques fondées sur l'apprentissage automatique sont avantageuses dans des scénarios réalistes o`u le nombre de données lors de l'étape d'apprentissage est faible. Par la suite, nous proposons une nouvelle métrique formelle d'évaluation qui permet (1) de comparer des attaques paramétriques et non-paramétriques et (2) d'interpréter les résultats de chaque méthode. La nouvelle mesure fournit les causes d'un taux de réussite élevé ou faible d'une attaque et, par conséquent, donne des pistes pour améliorer l'évaluation d'une implantation. Enfin, nous présentons des résultats expérimentaux sur des appareils non protégés et protégés. La premi`ere étude montre que l'apprentissage automatique a un taux de réussite plus élevé qu'une méthode paramétrique lorsque seules quelques données sont disponibles. La deuxi`eme expérience démontre qu'un dispositif protégé est attaquable avec une approche appartenant `a l'apprentissage automatique. La stratégie basée sur l'apprentissage automatique nécessite le m^eme nombre de données lors de la phase d'apprentissage que lorsque celle-ci attaque un produit non protégé. Nous montrons également que des méthodes paramétriques surestiment ou sous-estiment le niveau de sécurité fourni par l'appareil alors que l'approche basée sur l'apprentissage automatique améliore cette estimation.

En résumé, notre th`ese est que les attaques basées sur l'apprentissage automatique sont avantageuses par rapport aux techniques classiques lorsque la quantité d'information a priori sur l'appareil cible et le nombre de données lors de la phase d'apprentissage sont faibles.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

L'omniprésence de dispositifs interconnectés am`ene `a un intér^et massif pour la sécurité informatique fournie entre autres par le domaine de la cryptographie. Pendant des décennies, les spécialistes en cryptographie estimaient le niveau de sécurité d'un algorithme cryptographique indépendamment de son implantation dans un dispositif. Cependant, depuis la publication des attaques d'implantation en 1996, les attaques physiques sont devenues un domaine de recherche actif en considérant les propriétés physiques de dispositifs cryptographiques. Dans notre dissertation, nous nous concentrons sur les attaques profilées. Traditionnellement, les attaques profilées appliquent des méthodes paramétriques dans lesquelles une information a priori sur les propriétés physiques est supposée. Le domaine de l'apprentissage automatique produit des mod`eles automatiques et génériques ne nécessitant pas une information a priori sur le phénom`ene étudié.Cette dissertation apporte un éclairage nouveau sur les capacités des méthodes d'apprentissage automatique. Nous démontrons d'abord que les attaques profilées paramétriques surpassent les méthodes d'apprentissage automatique lorsqu'il n'y a pas d'erreur d'estimation ni d'hypoth`ese. En revanche, les attaques fondées sur l'apprentissage automatique sont avantageuses dans des scénarios réalistes o`u le nombre de données lors de l'étape d'apprentissage est faible. Par la suite, nous proposons une nouvelle métrique formelle d'évaluation qui permet (1) de comparer des attaques paramétriques et non-paramétriques et (2) d'interpréter les résultats de chaque méthode. La nouvelle mesure fournit les causes d'un taux de réussite élevé ou faible d'une attaque et, par conséquent, donne des pistes pour améliorer l'évaluation d'une implantation. Enfin, nous présentons des résultats expérimentaux sur des appareils non protégés et protégés. La premi`ere étude montre que l'apprentissage automatique a un taux de réussite plus élevé qu'une méthode paramétrique lorsque seules quelques données sont disponibles. La deuxi`eme expérience démontre qu'un dispositif protégé est attaquable avec une approche appartenant `a l'apprentissage automatique. La stratégie basée sur l'apprentissage automatique nécessite le m^eme nombre de données lors de la phase d'apprentissage que lorsque celle-ci attaque un produit non protégé. Nous montrons également que des méthodes paramétriques surestiment ou sous-estiment le niveau de sécurité fourni par l'appareil alors que l'approche basée sur l'apprentissage automatique améliore cette estimation. En résumé, notre th`ese est que les attaques basées sur l'apprentissage automatique sont avantageuses par rapport aux techniques classiques lorsque la quantité d'information a priori sur l'appareil cible et le nombre de données lors de la phase d'apprentissage sont faibles.

Hajingabo, Leon

Analyzing molecular network perturbations in human cancer: application to mutated genes and gene fusions involved in acute lymphoblastic leukemia PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209126b,
title = {Analyzing molecular network perturbations in human cancer: application to mutated genes and gene fusions involved in acute lymphoblastic leukemia},
author = {Leon Hajingabo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209126/3/d6d6225e-dfa8-46a3-985a-2ef19c71eff1.txt},
year = {2015},
date = {2015-01-01},
abstract = {Le séquenccage du génome humain et l'émergence de nouvelles technologies de génomique `a haut débit, ont initié de nouveaux mod`eles d'investigation pour l'analyse systématique des maladies humaines. Actuellement, nous pouvons tenter de comprendre les maladies tel que le cancer avec une perspective plus globale, en identifiant des g`enes responsables des cancers et en étudiant la mani`ere dont leurs produits protéiques fonctionnent dans un réseau d’interactions moléculaires. Dans ce contexte, nous avons collecté les g`enes spécifiquement liés `a la Leucémie Lymphoblastique Aigu"e (LLA), et identifié de nouveaux partenaires d'interaction qui relient ces g`enes clés associés `a la LLA tels que NOTCH1, FBW7, KRAS et PTPN11, dans un réseau d’interactions. Nous avons également tenté de prédire l’impact fonctionnel des variations génomiques tel que des fusions de g`enes impliquées dans LLA. En utilisant comme mod`eles trois différentes translocations chromosomiques ETV6-RUNX1 (TEL-AML1), BCR-ABL1, et E2A-PBX1 (TCF3-PBX1) fréquemment identifiées dans des cellules B LLA, nous avons adapté une approche de prédiction d’oncog`enes afin de prédire des perturbations moléculaires dans la LLA. Nous avons montré que les circuits transcriptomiques dépendant de Myc et JunD sont spécifiquement dérégulés suite aux fusions de g`enes TEL-AML1 et TCF3-PBX1, respectivement. Nous avons également identifié le mécanisme de transport des ARNm dépendant du facteur NXF1 comme une cible directe de la protéine de fusion TCF3-PBX1. Gr^ace `a cette approche combinant les données interactomiques et les analyses d'expression génique, nous avons fourni un nouvel aperccu `a la compréhension moléculaire de la Leucémie Lymphoblastique Aigu"e.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Huculeci, Radu; Garcia-Pino, Abel; Buts, Lieven; Lenaerts, Tom; van Nuland, Nico A J

Structural insights into the intertwined dimer of fyn SH2. Journal Article

In: Protein science, 24 (12), pp. 1964-1978, 2015, (DOI: 10.1002/pro.2806).

Abstract | Links | BibTeX

Zisis, Ioannis; Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom

Generocity motivated by acceptance - evolutionary analysis of an anticipation game Journal Article

In: Scientific Reports, 5 , 2015, (Language of publication: en).

Links | BibTeX

Gazzo, Andrea; Daneels, Dorien; Cilia, Elisa; Bonduelle, Maryse; Abramowicz, Marc; Dooren, Sonia Van; Smits, Guillaume; Lenaerts, Tom

DIDA: A curated and annotated digenic diseases database. Journal Article

In: Nucleic acids research, 2015, (DOI: 10.1093/nar/gkv1068).

Abstract | Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Santos, Francisco C; Lenaerts, Tom

Emergence of cooperation via intention recognition, commitment and apology-A research summary Journal Article

In: AI communications, 28 (4), pp. 709-715, 2015, (DOI: 10.3233/AIC-150672).

Abstract | Links | BibTeX

@article{info:hdl:2013/220724,
title = {Emergence of cooperation via intention recognition, commitment and apology-A research summary},
author = {The Anh T A H Han and Luís Moniz Pereira and Francisco C Santos and Tom Lenaerts},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/220724},
year = {2015},
date = {2015-01-01},
journal = {AI communications},
volume = {28},
number = {4},
pages = {709-715},
abstract = {The mechanisms of emergence and evolution of cooperation in populations of abstract individuals, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via evolutionary game theory, inspired in part on evolutionary psychology. Their systematic study resorts to simulation techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters and alternative virtual games. The theoretical and experimental results have continually been surprising, rewarding and promising. In our recent work, we initiated the introduction, in such groups of individuals, of cognitive abilities inspired on techniques and theories of Artificial Intelligence, namely those pertaining to Intention Recognition, Commitment and Apology (separately and jointly), encompassing errors in decision-making and communication noise. As a result, both the emergence and stability of cooperation become reinforced comparatively to the absence of such cognitive abilities. This holds separately for Intention Recognition, for Commitment and for Apology, and even more so when they are jointly engaged. Our presentation aims to sensitize the reader to these evolutionary game theory based issues, results and prospects, which are accruing in importance for the modeling of minds with machines, with impact on our understanding of the evolution of mutual tolerance and cooperation. Recognition of someone's intentions, which may include imagining the recognition others have of our own intentions, and may comprise not just some error tolerance, but also a penalty for unfulfilled commitment though allowing for apology, can lead to evolutionary stable win/win equilibriums within groups of individuals, and perhaps amongst groups. The recognition and the manifestation of intentions, plus the assumption of commitment-even whilst paying a cost for putting it in place-and the acceptance of apology, are all facilitators in that respect, each of them singly and, above all, in collusion.},
note = {DOI: 10.3233/AIC-150672},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

The mechanisms of emergence and evolution of cooperation in populations of abstract individuals, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via evolutionary game theory, inspired in part on evolutionary psychology. Their systematic study resorts to simulation techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters and alternative virtual games. The theoretical and experimental results have continually been surprising, rewarding and promising. In our recent work, we initiated the introduction, in such groups of individuals, of cognitive abilities inspired on techniques and theories of Artificial Intelligence, namely those pertaining to Intention Recognition, Commitment and Apology (separately and jointly), encompassing errors in decision-making and communication noise. As a result, both the emergence and stability of cooperation become reinforced comparatively to the absence of such cognitive abilities. This holds separately for Intention Recognition, for Commitment and for Apology, and even more so when they are jointly engaged. Our presentation aims to sensitize the reader to these evolutionary game theory based issues, results and prospects, which are accruing in importance for the modeling of minds with machines, with impact on our understanding of the evolution of mutual tolerance and cooperation. Recognition of someone's intentions, which may include imagining the recognition others have of our own intentions, and may comprise not just some error tolerance, but also a penalty for unfulfilled commitment though allowing for apology, can lead to evolutionary stable win/win equilibriums within groups of individuals, and perhaps amongst groups. The recognition and the manifestation of intentions, plus the assumption of commitment-even whilst paying a cost for putting it in place-and the acceptance of apology, are all facilitators in that respect, each of them singly and, above all, in collusion.

Martinez-Vaquero, Luis L A; Han, The Anh T A H; Pereira, Luís Marcelo; Lenaerts, Tom

Apology and forgiveness evolve to resolve failures in cooperative agreements Journal Article

In: Scientific reports, 5 , 2015, (DOI: 10.1038/srep10639).

Abstract | Links | BibTeX

Han, The Anh T A H; Santos, Francisco C; Lenaerts, Tom; Pereira, Luís Marcelo

Synergy between intention recognition and commitments in cooperation dilemmas Journal Article

In: Scientific Reports, 5 , 2015, (DOI: 10.1038/srep09312).

Abstract | Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

Avoiding or restricting defectors in public goods games? Journal Article

In: Journal of the Royal Society interface, 12 (103), pp. 20141203, 2015, (DOI: 10.1098/rsif.2014.1203).

Abstract | Links | BibTeX

Sekara, Mateusz; Kowalski, Michael; Byrski, Aleksander; Indurkhya, Bipin; Kisiel-Dorohinicki, Marek; Samson, Dana; Lenaerts, Tom

Multi-pheromone ant Colony Optimization for Socio-cognitive Simulation Purposes Journal Article

In: Procedia Computer Science, 51 , pp. 954-963, 2015, (DOI: 10.1016/j.procs.2015.05.234).

Abstract | Links | BibTeX

Colaprico, Antonio; Silva, Tiago Da; Olsen, Catharina; Garofano, Luciano; Cava, Claudia; Garolini, Davide; Sabedot, Thais TS; Malta, Tathiane TM; Pagnotta, Stefano SM; Castiglioni, Isabella; Ceccarelli, M; Bontempi, Gianluca; Noushmehr, Houtan

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Journal Article

In: Nucleic acids research, 2015, (DOI: 10.1093/nar/gkv1507).

Abstract | Links | BibTeX

@article{info:hdl:2013/222877,
title = {TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.},
author = {Antonio Colaprico and Tiago Da Silva and Catharina Olsen and Luciano Garofano and Claudia Cava and Davide Garolini and Thais TS Sabedot and Tathiane TM Malta and Stefano SM Pagnotta and Isabella Castiglioni and M Ceccarelli and Gianluca Bontempi and Houtan Noushmehr},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/222877},
year = {2015},
date = {2015-01-01},
journal = {Nucleic acids research},
abstract = {The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.},
note = {DOI: 10.1093/nar/gkv1507},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Han, The Anh T A H; Lenaerts, Tom

The efficient interaction of costly punishment and commitment Journal Article

In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 3 , pp. 1657-1658, 2015, (Language of publication: en).

Abstract | Links | BibTeX

Lerman, Liran; Bontempi, Gianluca; Markowitch, Olivier

The bias–variance decomposition in profiled attacks Journal Article

In: Journal of Cryptographic Engineering, 5 (4), pp. 255-267, 2015, (DOI: 10.1007/s13389-015-0106-1).

Abstract | Links | BibTeX

Faust, Karoline; Mendez, Gipsi Lima; Lerat, Jean-Sébastien; Sathirapongsasuti, Jarupon Fah; Knight, Rob; Huttenhower, Curtis; Lenaerts, Tom; Raes, Jeroen JR

Cross-biome comparison of microbial association networks Journal Article

In: Frontiers in microbiology, 6 (OCT), 2015, (DOI: 10.3389/fmicb.2015.01200).

Abstract | Links | BibTeX

Lerman, Liran; Poussier, Romain; Bontempi, Gianluca; Markowitch, Olivier; cc, Fran

Template attacks vs Machine Learning Revisited Journal Article

In: Lecture notes in computer science, 9064 , pp. 20-33, 2015, (Language of publication: en).

Links | BibTeX

Zisis, Ioannis; Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom

Generosity motivated by acceptance - evolutionary analysis of an anticipation game. Journal Article

In: Scientific reports, 5 , pp. 18076, 2015, (DOI: 10.1038/srep18076).

Abstract | Links | BibTeX

Lerman, Liran; Bontempi, Gianluca; Markowitch, Olivier

A machine learning approach against a masked AES: Reaching the limit of side-channel attacks with a learning model Journal Article

In: Journal of Cryptographic Engineering, 5 (2), pp. 123-139, 2015, (DOI: 10.1007/s13389-014-0089-3).

Abstract | Links | BibTeX

Olsen, Catharina; Fleming, Kathleen; Prendergast, Niall; Rubio, Renee; Emmert-Streib, Frank; Bontempi, Gianluca; Quackenbush, John; Haibe-Kains, Benjamin

Using shRNA experiments to validate gene regulatory networks Journal Article

In: Genomics Data, 4 , pp. 123-126, 2015, (DOI: 10.1016/j.gdata.2015.03.011).

Abstract | Links | BibTeX

Mendez, Gipsi Lima; Faust, Karoline; Henry, Lynn N; Decelle, Johan; Colin, Simon; Carcillo, Fabrizio; Chaffron, Samuel; Ignacio-Espinosa, Cesar JC; Roux, Simon; Vincent, Flora; Bittner, Lucie; Darzi, Youssef; Wang, Jun; Audic, Stéphane; Berline, Léo; Bontempi, Gianluca; Cabello, Ana AM; Coppola, Laurent; Cornejo-Castillo, Francisco FM; d'Ovidio, Francesco; Meester, Luc De; Ferrera, Isabel; Garet-Delmas, Marie-José; Guidi, Lionel; Lara, Elena; Pesant, Stéphane; Royo-Llonch, Marta; Salazar, Guillem; Sanchez, Paloma; Sebastian, Marta; Souffreau, Caroline; Dimier, Céline; Picheral, Marc; Searson, Sarah; Kandels-Lewis, Stefanie; coordinators, Tara Oceans; Gorsky, Gabriel; Not, Fabrice; Ogata, Hiroyuki; Speich, Sabrina; Stemmann, Lars; Weissenbach, Jean; Wincker, Patrick; Acinas, Silvia SG; Sunagawa, Shinichi; Bork, Peer; Sullivan, Matthew B; Karsenti, Eric; Bowler, Chris; de Vargas, Colomban; Raes, Jeroen JR

Ocean plankton. Determinants of community structure in the global plankton interactome. Journal Article

In: Science, 348 (6237), pp. 1262073, 2015, (DOI: 10.1126/science.1262073).

Abstract | Links | BibTeX

@article{info:hdl:2013/200950,
title = {Ocean plankton. Determinants of community structure in the global plankton interactome.},
author = {Gipsi Lima Mendez and Karoline Faust and Lynn N Henry and Johan Decelle and Simon Colin and Fabrizio Carcillo and Samuel Chaffron and Cesar JC Ignacio-Espinosa and Simon Roux and Flora Vincent and Lucie Bittner and Youssef Darzi and Jun Wang and Stéphane Audic and Léo Berline and Gianluca Bontempi and Ana AM Cabello and Laurent Coppola and Francisco FM Cornejo-Castillo and Francesco d'Ovidio and Luc De Meester and Isabel Ferrera and Marie-José Garet-Delmas and Lionel Guidi and Elena Lara and Stéphane Pesant and Marta Royo-Llonch and Guillem Salazar and Paloma Sanchez and Marta Sebastian and Caroline Souffreau and Céline Dimier and Marc Picheral and Sarah Searson and Stefanie Kandels-Lewis and Tara Oceans coordinators and Gabriel Gorsky and Fabrice Not and Hiroyuki Ogata and Sabrina Speich and Lars Stemmann and Jean Weissenbach and Patrick Wincker and Silvia SG Acinas and Shinichi Sunagawa and Peer Bork and Matthew B Sullivan and Eric Karsenti and Chris Bowler and Colomban de Vargas and Jeroen JR Raes},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/200950},
year = {2015},
date = {2015-01-01},
journal = {Science},
volume = {348},
number = {6237},
pages = {1262073},
abstract = {Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.},
note = {DOI: 10.1126/science.1262073},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Colaprico, Antonio; Cava, Claudia; Bertoli, Gloria; Bontempi, Gianluca; Castiglioni, Isabella

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer Journal Article

In: BioMed Research International, 2015 , 2015, (DOI: 10.1155/2015/831314).

Abstract | Links | BibTeX

Bontempi, Gianluca; Flauder, Maxime

From dependency to causality: a machine learning approach Journal Article

In: Journal of machine learning research, 2015, (Language of publication: en).

Abstract | Links | BibTeX

Lerman, Liran; Poussier, Romain; Bontempi, Gianluca; Markowitch, Olivier; cc, Fran

Template attacks vs. Machine learning revisited (and the curse of dimensionality in side-channel analysis) Journal Article

In: Lecture notes in computer science, 9064 , pp. 20-33, 2015, (DOI: 10.1007/978-3-319-21476-4_2).

Abstract | Links | BibTeX

Pozzolo, Andrea Dal; Caelen, Olivier; Bontempi, Gianluca

When is undersampling effective in unbalanced classification tasks? Journal Article

In: Lecture notes in computer science, 9284 , pp. 200-215, 2015, (DOI: 10.1007/978-3-319-23528-8_13).

Abstract | Links | BibTeX

Cuellar, M P M P; Ros, Maria; Bautista, Maria Martin J; "e, Yann-A; Bontempi, Gianluca

An approach for the evaluation of human activities in physical therapy scenarios Journal Article

In: Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 141 , pp. 401-414, 2015, (DOI: 10.1007/978-3-319-16292-8_29).

Abstract | Links | BibTeX

Pozzolo, Andrea Dal; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca

Credit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information Inproceedings

In: Neural Networks (IJCNN), 2015 International Joint Conference on, 2015, (Language of publication: en).

Links | BibTeX

Pozzolo, Andrea Dal; Caelen, Olivier; Johnson, Reid; Bontempi, Gianluca

Calibrating Probability with Undersampling for Unbalanced Classification Inproceedings

In: 2015 IEEE Symposium on Computational Intelligence and Data Mining, 2015, (Language of publication: en).

Links | BibTeX

Zisis, Ioannis; Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom

Receivers’ acceptance drives the generosity of dictators in a dictator game with partner selection Miscellaneous

2015, (Conference: Student Conference on Complexity Science(9-11 September 2015: Granada, Spain)).

Links | BibTeX

Zisis, Ioannis; Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom

An experimental and evolutionary analysis of group formation and gift giving Miscellaneous

2015, (Conference: Conference on Complex Systems(28 September - 2 October 2015: Tempe, USA)).

Links | BibTeX

Zisis, Ioannis; Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom

Receivers’ acceptance drives the generosity of dictators in a dictator game with partner selection Miscellaneous

2015, (Conference: Physics Meets the Social Science, Granada Seminar on Computational and Statistical Physics(15-19 june 2015: La Herradura, Spain)).

Links | BibTeX

Martinez-Vaquero, Luis L A; Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom

Emergence of revenge and forgiveness in commitments Miscellaneous

2015, (Conference: Physics Meets the Social Science, Granada Seminar on Computational and Statistical Physics(15-19 June 2015: La Herradura, Spain)).

Links | BibTeX

Gazzo, Andrea; Daneels, Dorien; Smits, Guillaume; Dooren, Sonia Van; Cilia, Elisa; Lenaerts, Tom

DIDA - A first digenic diseases database Miscellaneous

2015, (Conference: 15th Annual Meeting of the Belgian Society of Human Genetics(6 May 2015: Charleroi, Belgium)).

Links | BibTeX

Conard, Ashley M; Cilia, Elisa; Lenaerts, Tom

Determining the winning SH3 coalition: how cooperative game theory reveals the importance of domain residues in peptide binding Miscellaneous

2015, (Conference: The 23rd Annual International Conference on Intelligent Systems for Molecular Biology and the 14th European Conference on Computational Biology(10-14 July 2015: Dublin, ireland)).

Links | BibTeX

Gazzo, Andrea; Daneels, Dorien; Bonduelle, Maryse; Dooren, Sonia Van; Smits, Guillaume; Lenaerts, Tom

Predicting oligogenic effects using digenic disease data Miscellaneous

2015, (Conference: 10th BeNeLux Bioinformatics Conference(7-8 December 2015: Antwerp, Belgium)).

Links | BibTeX

Daneels, Dorien; Gazzo, Andrea; Bonduelle, Maryse; Smits, Guillaume; Cilia, Elisa; Lenaerts, Tom; Dooren, Sonia Van

DIDA: a first database on digenic diseases Miscellaneous

2015, (Conference: VUB DS-LSM PhD day(31 March 2015: Jette, Belgium)).

Links | BibTeX

Gazzo, Andrea; Daneels, Dorien; Smits, Guillaume; Dooren, Sonia Van; Cilia, Elisa; Lenaerts, Tom

DIDA - A first digenic diseases database Miscellaneous

2015, (Conference: Cold Spring Harbor Laboratory conference on Genome Informatics(28-31 October 2015: Cold Spring Harbor, USA)).

Links | BibTeX

Huculeci, Radu; Cilia, Elisa; Buts, Lieven; Houben, Klaartje; van Nuland, Nico A J; Lenaerts, Tom

SH2 sidechain dynamics may be key to unlock kinase activity Miscellaneous

2015, (Conference: Keystone symposium - The Biological Code of Cell Signaling: A Tribute to Tony Pawson(11-15 january 2015: Steamboat springs, Colorado, USA)).

Links | BibTeX

Ruano, Ana Zafra; Cilia, Elisa; Couceiro, José JR; Sanz, Javier Ruiz; Schymkowitz, Joost J; Rousseau, Frédéric; Luque, Irene; Lenaerts, Tom

On the evolutionary conservation of the dynamic changes predicted for a collection of SH3 domain structures Miscellaneous

2015, (Conference: Keystone symposium - The Biological Code of Cell Signaling: A Tribute to Tony Pawson(11-16 January 2015: Steamboat springs, Colorado, USA)).

Links | BibTeX

Lenaerts, Tom

Bits and bytes of protein domain communication Miscellaneous

2015, (Conference: International Conference on Emergence in Chemical Systems(21-27 june 2015: Anchorage, USA)).

Links | BibTeX

Lenaerts, Tom

To apologize or not to apologize; the evolutionary viability of forgiveness in interrupted commitments in the iterated prisoners dilemma Miscellaneous

2015, (Conference: French Symposium on Games(26-30 May 2015: Paris, France)).

Links | BibTeX

Lenaerts, Tom

Commitment to cooperate - the evolutionary viability of individual and group commitments in social dilemmas Miscellaneous

2015, (Conference: Granada Seminar on Computational and Statistical Physics(15-19 june 2015: La Herradura, Spain)).

Links | BibTeX

Hoffman, Anthony; Lenaerts, Tom

Live migration of virtual machines in the cloud computing. Informatique Masters Thesis

2015, (Language of publication: fr).

Links | BibTeX

Pozzolo, Andrea Dal

Adaptive Machine Learning for Credit Card Fraud Detection PhD Thesis

2015, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

2014

Lenaerts, Tom; Giacobini, Mario; Bersini, Hugues; Bourgine, Paul; Dorigo, Marco; Doursat, René

Special issue for the 20th anniversary of the European conference on artificial life (ECAL 2011) Book

2014, (Language of publication: fr).

Links | BibTeX

Taieb, Souhaib Ben

Machine learning strategies for multi-step-ahead time series forecasting PhD Thesis

2014, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209234,
title = {Machine learning strategies for multi-step-ahead time series forecasting},
author = {Souhaib Ben Taieb},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209234},
year = {2014},
date = {2014-01-01},
abstract = {How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series.

Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.

The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.

Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.

In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.

Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.

We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.

Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.

},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series. Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.

Taieb, Souhaib Ben

Machine learning strategies for multi-step-ahead time series forecasting PhD Thesis

2014, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209234b,
title = {Machine learning strategies for multi-step-ahead time series forecasting},
author = {Souhaib Ben Taieb},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209234/4/2c5e8bfe-3eab-4c2a-acb0-843504ddfcbd.txt},
year = {2014},
date = {2014-01-01},
abstract = {How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series.

},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Cilia, Elisa; Teso, Stefano; Ammendola, Sergio; Lenaerts, Tom; Passerini, Andrea

Predicting virus mutations through statistical relational learning. Journal Article

In: BMC bioinformatics, 15 , pp. 309, 2014, (DOI: 10.1186/1471-2105-15-309).

Abstract | Links | BibTeX

Rubio, Lucia; Huculeci, Radu; Buts, Lieven; Vanwetswinkel, Sophie; Lenaerts, Tom; van Nuland, Nico A J

(1)H, (13)C, and (15)N backbone and side-chain chemical shift assignments of the free and bound forms of the human PTPN11 second SH2 domain. Journal Article

In: Biomolecular N M R Assignments, 8 , 2014, (DOI: 10.1007/s12104-013-9504-4).

Abstract | Links | BibTeX

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim

The DynaMine webserver: predicting protein dynamics from sequence. Journal Article

In: Nucleic acids research, 42 , pp. W264-W270, 2014, (DOI: 10.1093/nar/gku270).

Abstract | Links | BibTeX

Gagliolo, Matteo; Lenaerts, Tom; Jacobs, Dirk

Politics Matters: Dynamics of Inter-organizational Networks among Immigrant Associations Journal Article

In: Studies in Computational Intelligence, 549 , pp. 47-55, 2014, (DOI: 10.1007/978-3-319-05401-8_5).

Abstract | Links | BibTeX

Lenaerts, Tom; Giacobini, Mario; Bersini, Hugues; Bourgine, Paul; Dorigo, Marco; Doursat, René

Special issue for the 20th anniversary of the European conference on artificial life (ECAL 2011): Editorial Journal Article

In: Artificial life, 20 (1), pp. 1-3, 2014, (DOI: 10.1162/ARTL-a-00093).

Links | BibTeX

Trepo, Eric; Nahon, Pierre; Bontempi, Gianluca; Valenti, Luca; Falleti, Edmondo; Nischalke, Hans Dieter; Hamza, Samia; Corradini, Stefano Ginanni; Burza, Maria Antonella; Guyot, Erwan; Donati, Benedetta; Spengler, Ulrich; Hillon, Patrick; Toniutto, Pierluigi; Henrion, Jean; Franchimont, Denis; `e, Jacques Devi; Mathurin, Philippe; Moreno, Christophe; Romeo, Stefano; Deltenre, Pierre

Association between the PNPLA3 (rs738409 C>G) variant and hepatocellular carcinoma: Evidence from a meta-analysis of individual participant data. Journal Article

In: Hepatology, 59 (6), pp. 2170-2177, 2014, (DOI: 10.1002/hep.26767).

Abstract | Links | BibTeX

@article{info:hdl:2013/196264,
title = {Association between the PNPLA3 (rs738409 C>G) variant and hepatocellular carcinoma: Evidence from a meta-analysis of individual participant data.},
author = {Eric Trepo and Pierre Nahon and Gianluca Bontempi and Luca Valenti and Edmondo Falleti and Hans Dieter Nischalke and Samia Hamza and Stefano Ginanni Corradini and Maria Antonella Burza and Erwan Guyot and Benedetta Donati and Ulrich Spengler and Patrick Hillon and Pierluigi Toniutto and Jean Henrion and Denis Franchimont and Jacques Devi{`e}re and Philippe Mathurin and Christophe Moreno and Stefano Romeo and Pierre Deltenre},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/196264/3/196264.pdf},
year = {2014},
date = {2014-01-01},
journal = {Hepatology},
volume = {59},
number = {6},
pages = {2170-2177},
abstract = {The incidence of hepatocellular carcinoma (HCC) is increasing in Western countries. Although several clinical factors have been identified, many individuals never develop HCC, suggesting a genetic susceptibility. However, to date, only a few single-nucleotide polymorphisms have been reproducibly shown to be linked to HCC onset. A variant (rs738409 C>G, encoding for p.I148M) in the PNPLA3 gene is associated with liver damage in chronic liver diseases. Interestingly, several studies have reported that the minor rs738409[G] allele is more represented in HCC cases in chronic hepatitis C (CHC) and alcoholic liver disease (ALD). However, a significant association with HCC related to CHC has not been consistently observed, and the strength of the association between rs738409 and HCC remains unclear. We performed a meta-analysis of individual participant data including 2,503 European patients with cirrhosis to assess the association between rs738409 and HCC, particularly in ALD and CHC. We found that rs738409 was strongly associated with overall HCC (odds ratio [OR] per G allele, additive model=1.77; 95% confidence interval [CI]: 1.42-2.19; P=2.78 ?x 10(-7) ). This association was more pronounced in ALD (OR=2.20; 95% CI: 1.80-2.67; P=4.71 ?x 10(-15) ) than in CHC patients (OR=1.55; 95% CI: 1.03-2.34; P=3.52 ?x 10(-2) ). After adjustment for age, sex, and body mass index, the variant remained strongly associated with HCC.},
note = {DOI: 10.1002/hep.26767},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Lerman, Liran; Medeiros, Stéphane Fernandes; Bontempi, Gianluca; Markowitch, Olivier

A Machine Learning Approach Against a Masked AES Journal Article

In: Lecture notes in computer science, 8419 , pp. 61-75, 2014, (Language of publication: en).

Links | BibTeX

Lopes, Miguel; Kutlu, Burak; Miani, MICHELA; Bang-Berthelsen, Claus H; Størling, Joachim; Pociot, Flemming; Goodman, Nathan; Hood, Lee; Welsh, Nils; Bontempi, Gianluca; Eizirik, Decio L

Temporal profiling of cytokine-induced genes in pancreatic beta-cells by meta-analysis and network inference. Journal Article

In: Genomics, 2014, (DOI: 10.1016/j.ygeno.2013.12.007).

Abstract | Links | BibTeX

Lerman, Liran; Bontempi, Gianluca; Markowitch, Olivier

Power analysis attack: an approach based on machine learning Journal Article

In: International Journal of Applied Cryptography, 3 (2), pp. 97-115, 2014, (DOI: 10.1504/IJACT.2014.062722).

Abstract | Links | BibTeX

Olsen, Catharina; Fleming, Kathleen; Prendergast, Niall; Rubio, Renee; Emmert-Streib, Frank; Bontempi, Gianluca; Haibe-Kains, Benjamin; Quackenbush, John

Inference and validation of predictive gene networks from biomedical literature and gene expression data Journal Article

In: Genomics, 103 , pp. 329-336, 2014, (DOI: 10.1016/j.ygeno.2014.03.004).

Abstract | Links | BibTeX

Olsen, Catharina; Bontempi, Gianluca; Emmert-Streib, Frank; Quackenbush, John; Haibe-Kains, Benjamin

Relevance of different prior knowledge sources for inferring gene interaction networks Journal Article

In: Frontiers in Genetics, 5 (177), 2014, (DOI: 10.3389/fgene.2014.00177).

Abstract | Links | BibTeX

@article{info:hdl:2013/172097,
title = {Relevance of different prior knowledge sources for inferring gene interaction networks},
author = {Catharina Olsen and Gianluca Bontempi and Frank Emmert-Streib and John Quackenbush and Benjamin Haibe-Kains},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/172097/3/PMC4067568.pdf},
year = {2014},
date = {2014-01-01},
journal = {Frontiers in Genetics},
volume = {5},
number = {177},
abstract = {When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F -scores. Furthermore, their integration with genomic data leads to a significant increase in F -scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets. © 2014 Olsen, Bontempi, Emmert-Streib, Quackenbush and Haibe-Kains.},
note = {DOI: 10.3389/fgene.2014.00177},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F -scores. Furthermore, their integration with genomic data leads to a significant increase in F -scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets. © 2014 Olsen, Bontempi, Emmert-Streib, Quackenbush and Haibe-Kains.

Lopes, Miguel; Bontempi, Gianluca

On the null distribution of the precision and recall curve Journal Article

In: Lecture notes in computer science, 8725 LNAI (PART 2), pp. 322-337, 2014, (DOI: 10.1007/978-3-662-44851-9_21).

Abstract | Links | BibTeX

Pozzolo, Andrea Dal; "e, Yann-A; Bontempi, Gianluca; Caelen, Olivier; Waterschoot, Serge

Learned lessons in credit card fraud detection from a practitioner perspective Journal Article

In: Expert systems with applications, 41 (10), pp. 4915-4928, 2014, (DOI: 10.1016/j.eswa.2014.02.026).

Abstract | Links | BibTeX

Pozzolo, Andrea Dal; Johnson, Reid; Caelen, Olivier; Waterschoot, Serge S; Chawla, Nitesh V; Bontempi, Gianluca

Using HDDT to avoid instances propagation in unbalanced and evolving data streams Inproceedings

In: Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 588-594, 2014, (Language of publication: en).

Links | BibTeX

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim

DynaMine: Sequence-based Protein Backbone Dynamics and Disorder Prediction Miscellaneous

2014, (Conference: 11th Bioinformatics ITalian Society annual meeting(26-28 February 2014: Rome, Italy)).

Links | BibTeX

Gazzo, Andrea; Daneels, Dorien; Smits, Guillaume; Dooren, Sonia Van; Cilia, Elisa; Lenaerts, Tom

DIDA: A first database on digenic diseases Miscellaneous

2014, (Conference: Benelux Bioinformatics Conference(8-9 December 2014: Luxembourg)).

Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Santos, Francisco C; Lenaerts, Tom

Evolution of Pairwise Commitment and Cooperation Miscellaneous

2014, (Conference: European Conference on Complex Systems(22-26 September 2014: Lucca, Italy)).

Links | BibTeX

Martinez-Vaquero, Luis L A; Han, The Anh T A H; Lenaerts, Tom

Promotion of cooperation through commitments in repeated games Miscellaneous

2014, (Conference: Spatial Human Cooperation: from theory to experiments and back(26-28 May 2014: Plon, Germany)).

Links | BibTeX

Huculeci, Radu; Cilia, Elisa; Buts, Lieven; Houben, Klaartje; van Nuland, Nico A J; Lenaerts, Tom

Unravelling the Intra-Protein Communication Pathway within the Fyn SH2 Domain Miscellaneous

2014, (Conference: Annual VIB Seminar(29-30 April 2014: Blankenberge, Belgium)).

Links | BibTeX

Vranken, Wim; Lenaerts, Tom; Cilia, Elisa

Deriving networks of reciprocal amino acid influences on dynamics and secondary structure Miscellaneous

2014, (Conference: BeNeLux Bioinformatics Conference(8-9 December 2014: Luxembourg)).

Links | BibTeX

Raimondi, Daniele; Lenaerts, Tom; Rooman, Marianne; Vranken, Wim

Decriminative sequence alignments for the prediction of snps and indels functions effects on proteins Miscellaneous

2014, (Conference: Benelux Bioinformatics Conference(8-9 December 2014: Luxembourg)).

Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom; Santos, Francisco C

Learning to Recognize Intentions Resolves Cooperation dilemma Miscellaneous

2014, (Conference: 23rd annual Belgian-Dutch Conference on Machine Learning(6 june 2014: Brussels, Belgium)).

Links | BibTeX

Conard, Ashley M; Cilia, Elisa; Lenaerts, Tom

Determining the winning SH3 coalition: how cooperative game theory reveals the importance of domain residues in peptide binding Miscellaneous

2014, (Conference: Benelux Bioinformatics Conference(9th: 8-9 December 2014: Luxembourg)).

Links | BibTeX

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

Dynamine: a web-server for predicting protein dynamics from sequence Miscellaneous

2014, (Conference: The 13th European Conference on Computational Biology(7-10 September 2014: Strassbourg, France)).

Links | BibTeX

Cilia, Elisa; Raimondi, Daniele; Lenaerts, Tom; Vranken, Wim

Applying dynamics-based interaction potentials in a residue network Miscellaneous

2014, (Conference: 13th European Conference on Computational Biology(7-10 September 2014: Strassbourg, france)).

Links | BibTeX

Boes, Olivier; Lenaerts, Tom

Improving the Needleman-Wunsch algorithm with the Dynamine predictor. Informatique Masters Thesis

2014, (Language of publication: fr).

Links | BibTeX

Wintjens, Florian; Lenaerts, Tom

Machine Learning: Coalition-based naive bayesian classification. Informatique Masters Thesis

2014, (Language of publication: fr).

Links | BibTeX

Estievenart, Quentin; Lenaerts, Tom

Predicting secondary structure with dynamine. Informatique Masters Thesis

2014, (Language of publication: fr).

Links | BibTeX

Reggiani, Claudio; "e, Yann-A; Pozzolo, Andrea Dal; Olsen, Catharina; Bontempi, Gianluca

Minimum Redundancy Maximum Relevance: MapReduce implementation using Apache Hadoop Miscellaneous

2014, (Conference: Benelearn 2014).

Links | BibTeX

2013

Olsen, Catharina

Causal inference and prior integration in bioinformatics using information theory PhD Thesis

2013, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209401,
title = {Causal inference and prior integration in bioinformatics using information theory},
author = {Catharina Olsen},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209401/1/ba9583ce-51e9-4718-b438-fb816d60aea4.txt},
year = {2013},
date = {2013-01-01},
abstract = {An important problem in bioinformatics is the reconstruction of gene regulatory networks from expression data. The analysis of genomic data stemming from high- throughput technologies such as microarray experiments or RNA-sequencing faces several difficulties. The first major issue is the high variable to sample ratio which is due to a number of factors: a single experiment captures all genes while the number of experiments is restricted by the experiment’s cost, time and patient cohort size. The second problem is that these data sets typically exhibit high amounts of noise.

Another important problem in bioinformatics is the question of how the inferred networks’ quality can be evaluated. The current best practice is a two step procedure. In the first step, the highest scoring interactions are compared to known interactions stored in biological databases. The inferred networks passes this quality assessment if there is a large overlap with the known interactions. In this case, a second step is carried out in which unknown but high scoring and thus promising new interactions are validated ’by hand’ via laboratory experiments. Unfortunately when integrating prior knowledge in the inference procedure, this validation procedure would be biased by using the same information in both the inference and the validation. Therefore, it would no longer allow an independent validation of the resulting network.

The main contribution of this thesis is a complete computational framework that uses experimental knock down data in a cross-validation scheme to both infer and validate directed networks. Its components are i) a method that integrates genomic data and prior knowledge to infer directed networks, ii) its implementation in an R/Bioconductor package and iii) a web application to retrieve prior knowledge from PubMed abstracts and biological databases. To infer directed networks from genomic data and prior knowledge, we propose a two step procedure: First, we adapt the pairwise feature selection strategy mRMR to integrate prior knowledge in order to obtain the network’s skeleton. Then for the subsequent orientation phase of the algorithm, we extend a criterion based on interaction information to include prior knowledge. The implementation of this method is available both as part of the prior retrieval tool Predictive Networks and as a stand-alone R/Bioconductor package named predictionet.

Furthermore, we propose a fully data-driven quantitative validation of such directed networks using experimental knock-down data: We start by identifying the set of genes that was truly affected by the perturbation experiment. The rationale of our validation procedure is that these truly affected genes should also be part of the perturbed gene’s childhood in the inferred network. Consequently, we can compute a performance score},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

An important problem in bioinformatics is the reconstruction of gene regulatory networks from expression data. The analysis of genomic data stemming from high- throughput technologies such as microarray experiments or RNA-sequencing faces several difficulties. The first major issue is the high variable to sample ratio which is due to a number of factors: a single experiment captures all genes while the number of experiments is restricted by the experiment’s cost, time and patient cohort size. The second problem is that these data sets typically exhibit high amounts of noise.Another important problem in bioinformatics is the question of how the inferred networks’ quality can be evaluated. The current best practice is a two step procedure. In the first step, the highest scoring interactions are compared to known interactions stored in biological databases. The inferred networks passes this quality assessment if there is a large overlap with the known interactions. In this case, a second step is carried out in which unknown but high scoring and thus promising new interactions are validated ’by hand’ via laboratory experiments. Unfortunately when integrating prior knowledge in the inference procedure, this validation procedure would be biased by using the same information in both the inference and the validation. Therefore, it would no longer allow an independent validation of the resulting network.The main contribution of this thesis is a complete computational framework that uses experimental knock down data in a cross-validation scheme to both infer and validate directed networks. Its components are i) a method that integrates genomic data and prior knowledge to infer directed networks, ii) its implementation in an R/Bioconductor package and iii) a web application to retrieve prior knowledge from PubMed abstracts and biological databases. To infer directed networks from genomic data and prior knowledge, we propose a two step procedure: First, we adapt the pairwise feature selection strategy mRMR to integrate prior knowledge in order to obtain the network’s skeleton. Then for the subsequent orientation phase of the algorithm, we extend a criterion based on interaction information to include prior knowledge. The implementation of this method is available both as part of the prior retrieval tool Predictive Networks and as a stand-alone R/Bioconductor package named predictionet.Furthermore, we propose a fully data-driven quantitative validation of such directed networks using experimental knock-down data: We start by identifying the set of genes that was truly affected by the perturbation experiment. The rationale of our validation procedure is that these truly affected genes should also be part of the perturbed gene’s childhood in the inferred network. Consequently, we can compute a performance score

Bontempi, Gianluca; Taieb, Souhaib Ben

Statistical foundations of machine learning Book

Otexts, Online Books, 2013, (Language of publication: fr).

Links | BibTeX

Olsen, Catharina; Haibe-Kains, Benjamin; Quackenbush, John; Bontempi, Gianluca

On the Integration of Prior Knowledge in the Inference of Regulatory Networks. Book Chapter

In: World Scientific, 2013, (Language of publication: fr).

Links | BibTeX

Olsen, Catharina

Causal inference and prior integration in bioinformatics using information theory PhD Thesis

2013, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209401b,
title = {Causal inference and prior integration in bioinformatics using information theory},
author = {Catharina Olsen},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209401/1/ba9583ce-51e9-4718-b438-fb816d60aea4.txt},
year = {2013},
date = {2013-01-01},
abstract = {An important problem in bioinformatics is the reconstruction of gene regulatory networks from expression data. The analysis of genomic data stemming from high- throughput technologies such as microarray experiments or RNA-sequencing faces several difficulties. The first major issue is the high variable to sample ratio which is due to a number of factors: a single experiment captures all genes while the number of experiments is restricted by the experiment’s cost, time and patient cohort size. The second problem is that these data sets typically exhibit high amounts of noise.

Govorun, Maria

Pension and health insurance, phase-type modeling PhD Thesis

2013, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209447,
title = {Pension and health insurance, phase-type modeling},
author = {Maria Govorun},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209447/1/ffb25fee-0dd2-47b6-85b3-5b9db8df6d5a.txt},
year = {2013},
date = {2013-01-01},
abstract = {Depuis longtemps les mod`eles de type phase sont utilisés dans plusieurs domaines scientifiques pour décrire des syst`emes qui peuvent ^etre caractérisés par différents états. Les mod`eles sont bien connus en théorie des files d’attentes, en économie et en assurance.

La th`ese est focalisée sur différentes applications des mod`eles de type phase en assurance et montre leurs avantages. En particulier, le mod`ele de Lin et Liu en 2007 est intéressant, parce qu’il décrit le processus de vieillissement de l’organisme humain. La durée de vie d’un individu suit une loi de type phase et les états de ce mod`ele représentent des états de santé. Le fait que le mod`ele prévoit la connexion entre les états de santé et l’^age de l’individu le rend tr`es utile en assurance.

Les résultats principaux de la th`ese sont des nouveaux mod`eles et méthodes en assurance pension et en assurance santé qui utilisent l’hypoth`ese de la loi de type phase pour décrire la durée de vie d’un individu.

En assurance pension le but d’estimer la profitabilité d’un fonds de pension. Pour cette raison, on construit un mod`ele « profit-test » qui demande la modélisation de plusieurs caractéristiques. On décrit l’évolution des participants du fonds en adaptant le mod`ele du vieillissement aux causes multiples de sortie. L’estimation des profits futurs exige qu’on détermine les valeurs des cotisations pour chaque état de santé, ainsi que l’ancienneté et l’état de santé initial pour chaque participant. Cela nous permet d’obtenir la distribution de profits futurs et de développer des méthodes pour estimer les risques de longevité et de changements de marché. De plus, on suppose que la diminution des taux de mortalité pour les pensionnés influence les profits futurs plus que pour les participants actifs. C’est pourquoi, pour évaluer l’impact de changement de santé sur la profitabilité, on modélise séparément les profits venant des pensionnés.

En assurance santé, on utilise le mod`ele de type phase pour calculer la distribution de la valeur actualisée des co^uts futurs de santé. On développe des algorithmes récursifs qui permettent d’évaluer la distribution au cours d’une période courte, en utilisant des mod`eles fluides en temps continu, et pendant toute la durée de vie de l’individu, en construisant des mod`eles en temps discret. Les trois mod`eles en temps discret correspondent `a des hypoth`eses différentes qu’on fait pour les co^uts: dans le premier mod`ele on suppose que les co^uts de santé sont indépendants et identiquement distribués et ne dépendent pas du vieillissement de l’individu; dans les deux autres mod`eles on suppose que les co^uts dépendent de son état de santé.

},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Depuis longtemps les mod`eles de type phase sont utilisés dans plusieurs domaines scientifiques pour décrire des syst`emes qui peuvent ^etre caractérisés par différents états. Les mod`eles sont bien connus en théorie des files d’attentes, en économie et en assurance.La th`ese est focalisée sur différentes applications des mod`eles de type phase en assurance et montre leurs avantages. En particulier, le mod`ele de Lin et Liu en 2007 est intéressant, parce qu’il décrit le processus de vieillissement de l’organisme humain. La durée de vie d’un individu suit une loi de type phase et les états de ce mod`ele représentent des états de santé. Le fait que le mod`ele prévoit la connexion entre les états de santé et l’^age de l’individu le rend tr`es utile en assurance.Les résultats principaux de la th`ese sont des nouveaux mod`eles et méthodes en assurance pension et en assurance santé qui utilisent l’hypoth`ese de la loi de type phase pour décrire la durée de vie d’un individu.En assurance pension le but d’estimer la profitabilité d’un fonds de pension. Pour cette raison, on construit un mod`ele « profit-test » qui demande la modélisation de plusieurs caractéristiques. On décrit l’évolution des participants du fonds en adaptant le mod`ele du vieillissement aux causes multiples de sortie. L’estimation des profits futurs exige qu’on détermine les valeurs des cotisations pour chaque état de santé, ainsi que l’ancienneté et l’état de santé initial pour chaque participant. Cela nous permet d’obtenir la distribution de profits futurs et de développer des méthodes pour estimer les risques de longevité et de changements de marché. De plus, on suppose que la diminution des taux de mortalité pour les pensionnés influence les profits futurs plus que pour les participants actifs. C’est pourquoi, pour évaluer l’impact de changement de santé sur la profitabilité, on modélise séparément les profits venant des pensionnés.En assurance santé, on utilise le mod`ele de type phase pour calculer la distribution de la valeur actualisée des co^uts futurs de santé. On développe des algorithmes récursifs qui permettent d’évaluer la distribution au cours d’une période courte, en utilisant des mod`eles fluides en temps continu, et pendant toute la durée de vie de l’individu, en construisant des mod`eles en temps discret. Les trois mod`eles en temps discret correspondent `a des hypoth`eses différentes qu’on fait pour les co^uts: dans le premier mod`ele on suppose que les co^uts de santé sont indépendants et identiquement distribués et ne dépendent pas du vieillissement de l’individu; dans les deux autres mod`eles on suppose que les co^uts dépendent de son état de santé.

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim

From protein sequence to dynamics and disorder with DynaMine. Journal Article

In: Nature communications, 4 , pp. 2741, 2013, (DOI: 10.1038/ncomms3741).

Abstract | Links | BibTeX

Traulsen, Arne; Lenaerts, Tom; Pacheco, Jorge M J M; Dingli, David

On the dynamics of neutral mutations in a mathematical model for a homogeneous stem cell population. Journal Article

In: Journal of the Royal Society, Interface / the Royal Society, 10 (79), pp. 20120810, 2013, (DOI: 10.1098/rsif.2012.0810).

Abstract | Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Santos, Francisco C; Lenaerts, Tom

Good agreements make good friends. Journal Article

In: Scientific reports, 3 , pp. 2695, 2013, (DOI: 10.1038/srep02695).

Abstract | Links | BibTeX

Haibe-Kains, Benjamin; Desmedt, Christine; Leo, Angelo Di; Azambuja, Evandro; Larsimont, Denis; Selleslags, Jean; Delaloge, Suzette; Duhem, Caroline; Kains, Jean-Pierre; Carly, Birgit; Maerevoet, Marie; Vindevoghel, Anita; Rouas, Ghizlane; cc, Fran; Durbecq, Virginie; Cardoso, Fatima; Salgado, Roberto; Rovere, Rodrigo Kraft; Bontempi, Gianluca; Michiels, Stefan; Buyse, Marc; Nogaret, Jean-Marie; Qi, Yuan; Symmans, William Fraser; Pusztai, Lajos; D'Hondt, Veronique; Piccart-Gebhart, Martine; Sotiriou, Christos

Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients Journal Article

In: Genomics Data, 1 , pp. 7-10, 2013, (DOI: 10.1016/j.gdata.2013.09.001).

Abstract | Links | BibTeX

Bonnechere, Bruno; Wermenbol, Vanessa; Dan, Bernard; Salvia, Patrick; "e, Yann-A; Bontempi, Gianluca; Vansummeren, Stijn; Sholukha, Victor; Moiseev, Fedor; Jansen, Bart; Rooze, Marcel; Jan, Serge Van Sint

Management and interpretation of medical data related to cerebral pasly: the ICT4 Rehab project Journal Article

In: European journal of paediatric neurology, 17 (1), pp. 32, 2013, (Language of publication: na).

Links | BibTeX

Dedeurwaerder, Sarah; Defrance, Matthieu; Bizet, Martin; Calonne, Emilie; Bontempi, Gianluca; cc, Fran

A comprehensive overview of Infinium Human Methylation450 data processing Journal Article

In: Briefings in bioinformatics, 15 (6), pp. 929-941, 2013, (DOI: 0.1093/bib/bbt054).

Abstract | Links | BibTeX

Papillon-Cavanagh, Simon; Jay, Nicolas De; Hachem, Nehme; Olsen, Catharina; Bontempi, Gianluca; Aerts, Hugo J W L; Quackenbush, John; Haibe-Kains, Benjamin

Comparison and validation of genomic predictors for anticancer drug sensitivity. Journal Article

In: Journal of the American Medical Informatics Association, 20 (4), pp. 597-602, 2013, (DOI: 10.1136/amiajnl-2012-001442).

Abstract | Links | BibTeX

Lerman, Liran; Medeiros, Stéphane Fernandes; Veshchikov, Nikita; Meuter, Cédric; Bontempi, Gianluca; Markowitch, Olivier

Semi-Supervised Template Attack Journal Article

In: Lecture Notes in Computer Science, 7864 , pp. 184-199, 2013, (DOI: 10.1007/978-3-642-40026-1_12).

Abstract | Links | BibTeX

Jay, Nicolas De; Papillon-Cavanagh, Simon; Olsen, Catharina; El-Hachem, N; Bontempi, Gianluca; Haibe-Kains, Benjamin

mRMRe: an R package for parallelized mRMR ensemble feature selection Journal Article

In: Bioinformatics, 2013, (DOI: 10.1093/bioinformatics/btt383).

Abstract | Links | BibTeX

Bontempi, Gianluca; Taieb, Souhaib Ben; "e, Yann-A

Machine learning strategies for time series forecasting Journal Article

In: Lecture Notes in Business Information Processing, 138 LNBIP , pp. 62-77, 2013, (DOI: 10.1007/978-3-642-36318-4_3).

Abstract | Links | BibTeX

Lopes, Miguel; Bontempi, Gianluca

Experimental assessment of static and dynamic algorithms for gene regulation inference from time series expression data Journal Article

In: Frontiers in Genetics, 4 (DEC), 2013, (DOI: 10.3389/fgene.2013.00303).

Abstract | Links | BibTeX

Lerman, Liran; Markowitch, Olivier; Bontempi, Gianluca; Taieb, Souhaib Ben

A time series approach for profiling attack Journal Article

In: Lecture notes in computer science, 8204 , pp. 75-94, 2013, (DOI: 10.1007/978-3-642-41224-0_7).

Abstract | Links | BibTeX

Pozzolo, Andrea Dal; Caelen, Olivier; Waterschoot, Serge; Bontempi, Gianluca

Racing for unbalanced methods selection Journal Article

In: Lecture notes in computer science, 8206 LNCS , pp. 24-31, 2013, (DOI: 10.1007/978-3-642-41278-3_4).

Abstract | Links | BibTeX

Jan, Serge Van Sint; Wermenbol, Vanessa; Bogaert, Patrick Van; Desloovere, Kaat; Degelaen, Marc; Dan, Bernard; Salvia, P; Bonnechere, Bruno; "e, Yann-A; Bontempi, Gianluca; Vansummeren, Stijn; Sholukha, Victor; Moiseev, Fedor; Rooze, Marcel

Recherche intégrée relative `a l’appareil musculosquelettique : application `a la prise en charge clinique de l’infirmité motrice cérébrale (IMC) – le projet ICT4Rehab Journal Article

In: Médecine, 29 (5), pp. 529-536, 2013, (Language of publication: na).

Links | BibTeX

Lerat, Jean-Sébastien; Han, The Anh T A H; Lenaerts, Tom

Evolution of Common-Pool Resources and Social Welfare in Structured Populations Inproceedings

In: Joint Conference on Artificial Intelligence: Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2848-2854, Association for the Advancement of Artificial Intelligence, 2013, (Conference: (August 3-9 2013: Beijing)).

Abstract | Links | BibTeX

@inproceedings{info:hdl:2013/149489,
title = {Evolution of Common-Pool Resources and Social Welfare in Structured Populations},
author = {Jean-Sébastien Lerat and The Anh T A H Han and Tom Lenaerts},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/149489/1/IJCAI13-419.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Joint Conference on Artificial Intelligence: Twenty-Third International Joint Conference on Artificial Intelligence},
pages = {2848-2854},
publisher = {Association for the Advancement of Artificial Intelligence},
series = {Agent-Based and Multiagent Systems},
abstract = {The Common-pool resource (CPR) game is a social dilemma where agents have to decide how to consume a shared CPR. Either they each take their cut, completely destroying the CPR, or they restrain themselves, gaining less immediate profit but sustaining the resource and future profit. When no consumption takes place the CPR simply grows to its carrying capacity. As such, this dilemma provides a framework to study the evolution of social consumption strategies and the sustainability of resources, whose size adjusts dynamically through consumption and their own implicit population dynamics. The present study provides for the first time a detailed analysis of the evolutionary dynamics of consumption strategies in finite populations, focusing on the interplay between the resource levels and preferred consumption strategies. We show analytically which restrained consumers survive in relation to the growth rate of the resources and how this affects the resources' carrying capacity. Second, we show that population structures affect the sustainability of the resources and social welfare in the population. Current results provide an initial insight into the complexity of the CPR game, showing potential for a variety of different studies in the context of social welfare and resource sustainability.},
note = {Conference: (August 3-9 2013: Beijing)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Han, The Anh T A H; Pereira, Luís Moniz; Santos, F C; Lenaerts, Tom

Why is it so hard to say sorry: evolution of apology with commitment in the iterated Prisoner’s Dilemma Inproceedings

In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2013, (Language of publication: na).

Links | BibTeX

Management and interpretation of medical data related to Cerebral Palsy : the ICT4Rehab project. Inproceedings

In: EPNS, 2013, (Conference: (2013: Brussels, Belgium)).

Links | BibTeX

"e, Yann-A; Bontempi, Gianluca; Bonnechere, Bruno; Salvia, Patrick; Degelaen, Marc; Jan, Serge Van Sint

Data mining toolbox for gait analysis in children with cerebral palsy Inproceedings

In: ISB, 2013, (Conference: (2013: Natal, Brazil)).

Links | BibTeX

Jan, Serge Van Sint; Wermenbol, Vanessa; Dan, Bernard; Salvia, Patrick; Bonnechere, Bruno; "e, Yann-A; Bontempi, Gianluca; Vansummeren, Stijn; Sholukha, Victor; Moiseev, Fedor; Jansen, Bart; Rooze, Marcel

Développement d'instruments partagés pour la gestion et l'interprétation de données relatives `a la locomoation : le projet ICT4Rehab Inproceedings

In: SOFAMEA, 2013, (Conference: (2013: Luxembourg, Luxembourg)).

Links | BibTeX

"e, Yann-A; Bonnechere, Bruno; Salvia, Patrick; Jan, Serge Van Sint; Bontempi, Gianluca

Fouille de donnée pour l'analyse de la marche de patients atteints d'infirmité motrice cérébrale (ICT4Rehab) Inproceedings

In: SOFAMEA, 2013, (Conference: (2013: Luxembourg, Luxembourg)).

Links | BibTeX

Gagliolo, Matteo; Lenaerts, Tom; Jacobs, Dirk

A comparative analysis of the dynamics of interlocking directorates among immigrant organizations Miscellaneous

2013, (Conference: Belgian Social and Economic Network Research Meeting (BSEN) (3: 2013-10-03: Leuven)).

Links | BibTeX

Gagliolo, Matteo; Lenaerts, Tom; Jacobs, Dirk

A comparative analysis of the dynamics of interlock networks immigrant organizations Miscellaneous

2013, (Conference: ECPR General Conference (7: 2013-09-07: Bordeaux, France)).

Abstract | Links | BibTeX

@misc{info:hdl:2013/150672,
title = {A comparative analysis of the dynamics of interlock networks immigrant organizations},
author = {Matteo Gagliolo and Tom Lenaerts and Dirk Jacobs},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/150672/1/Gagliolo2013ECPR.pdf},
year = {2013},
date = {2013-01-01},
abstract = {Social capital is naturally embedded in social networks. In his famouswork on the causal relationship between ”bridging” social capital (e.g.,associational life), trust, and civic behavior, Putnam [1993] did not inves-tigate the structural aspect of such networks. Recently, the relationshipbetween associational life and civicness of ethnic minority groups in Eu-rope has been investigated [Fennema and Tillie, 2001; Jacobs et al., 2004;Vermeulen and Berger, 2008], without reaching uniform conclusions. Inthis work, simple structural properties of the network of interlocking di-rectorates among ethnic associations are used as a proxy of the socialcapital of the corresponding minority group. We pursue this line further,arguing that more advanced models may consistently predict differencesamong the studied communities, and look at the structure of such net-works,but also at the dynamics that produced it. Here we present resultswith a stochastic actor-based model, SIENA [Snijders et al., 2010], whichestimates the effect of actor covariates and local structure on networkevolution. We model the dynamics of the full two-mode network amongdirectors and boards of voluntary associations, including the structural ef-fects proposed by [Koskinen and Edling, 2012], and considering the politi-cal orientation of associations as a covariate. Using data from [Vermeulenand Berger, 2008], we compare the evolution of interlocks among Turkishassociations in two European capitals, and explain the noticeable differ-ence in structure by looking at statistically significant differences amongthe estimated effects. In the longer term we intend to relate the dynamicsof these networks to the civic behavior of the corresponding communities.},
note = {Conference: ECPR General Conference (7: 2013-09-07: Bordeaux, France)},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}

Lerat, Jean-Sébastien; Han, The Anh T A H; Lenaerts, Tom

Joint Conference on Artificial Intelligence: Twenty-Third International Joint Conference on Artificial Intelligence Miscellaneous

2013, (Conference: IJCAI (23: 3-7/08/2013: Beijing)).

Abstract | Links | BibTeX

Gagliolo, Matteo; Lenaerts, Tom; Jacobs, Dirk

A comparative analysis of the dynamics of interlocks among immigrant organizations Miscellaneous

2013, (Conference: Sunbelt, Social Networks Conference of the International Network for Social Network Analysis (INSNA) (XXXIII: 2013-05-23: Hamburg, Germany)).

Links | BibTeX

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

DynaMine: Sequence-based Protein Backbone Dynamics and Disorder Prediction Miscellaneous

2013, (Conference: 21st Annual International Conference on Intelligent Systems for Molecular Biology and the 12th European Conference on Computational Biology(19-23 july 2013: Berlin, Germany)).

Links | BibTeX

Huculeci, Radu; Cilia, Elisa; Buts, Lieven; Houben, Klaartje; van Nuland, Nico A J; Lenaerts, Tom

Unravelling the intra-protein communication pathway within the Fyn SH2 domain Miscellaneous

2013, (Conference: 12th Meeting of Young Belgian Magnetic Resonance Scientist(2-3 December 2013: Blankenberge, Belgium)).

Links | BibTeX

Cilia, Elisa; Vuister, Geerten W; Lenaerts, Tom

Accurate prediction of peptide-induced dynamical changes within the second PDZ domain of PTP1e Miscellaneous

2013, (Conference: 21st Annual International Conference on Intelligent Systems for Molecular Biology and the 12th European Conference on Computational Biology(19-23 July 2013: Berlin, Germany)).

Links | BibTeX

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim

DynaMine: From protein sequence to dynamics and disorder Miscellaneous

2013, (Conference: 8th Benelux Bioinformatics Conference(9-10 December 2013: Brussels, belgium)).

Links | BibTeX

Huculeci, Radu; Cilia, Elisa; Buts, Lieven; Houben, Klaartje; van Nuland, Nico A J; Lenaerts, Tom

Mapping intra-protein communication – The Fyn SH2 snap-lock mechanisms Miscellaneous

2013, (Conference: 8th Benelux Bioinformatics Conference(9-10 December 2013: Brussels, Belgium)).

Links | BibTeX

Han, The Anh T A H; Pereira, Luís Moniz; Santos, Francisco C; Lenaerts, Tom

Why is it so hard to say sorry ? Miscellaneous

2013, (Conference: 25th Benelux Artificial Intelligence Conference(7-8 November 2013: Deflt, the Netherlands)).

Links | BibTeX

Conard, Ashley M; Cilia, Elisa; Lenaerts, Tom

Cooperative game theory and feature selection to describe dependencies between amino acid within the SH3 Fyn protein domain Miscellaneous

2013, (Conference: The Grace Hopper Celebration (GHC) of Women in Computing(2-5 October 2013: Minneapolis, US)).

Links | BibTeX

Rubio, Lucia; Huculeci, Radu; Vanwetswinkel, Sophie; Buts, Lieven; Lenaerts, Tom; van Nuland, Nico A J

Unravelling the allosteric effects of SHP2’s SH2 domains in the through NMR relaxation experiments Miscellaneous

2013, (Conference: 12th Meeting of Young Belgian Magnetic Resonance Scientist(2-3 december 2013: Blankenberge, Belgium)).

Links | BibTeX

Antoine, Dewilde A D; Lenaerts, Tom

Infering Conditional Behaviors Using Probabilistic Models. Artificial Intelligence Masters Thesis

2013, (Language of publication: en).

Links | BibTeX

Marillet, Simon; Lenaerts, Tom

Evaluation of intra-protein co-evolution prediction methods. Informatique Masters Thesis

2013, (Language of publication: fr).

Links | BibTeX

Olsen, Catharina; Bontempi, Gianluca; Quackenbush, John; Haibe-Kains, Benjamin

Data-driven validation of gene regulatory networks using knock-down data Miscellaneous

2013, (Conference: 8th Benelux Bioinformatics Conference (BBC13)).

Links | BibTeX

Vion, Jérémy; Lenaerts, Tom

The effects of matching algorithms on the level of fairness in the ultimatum game. Informatique Masters Thesis

2013, (Language of publication: fr).

Links | BibTeX

Olsen, Catharina; Bontempi, Gianluca; Quackenbush, John; Haibe-Kains, Benjamin

Data-driven validation of gene regulatory networks using knock-down data Miscellaneous

2013, (Conference: 8th Benelux Bioinformatics Conference (BBC13)).

Links | BibTeX

Lerat, Jean-Sébastien; Lenaerts, Tom

Exploring Common-pool resources with natural resource dynamics. Informatique Masters Thesis

2013, (Language of publication: fr).

Links | BibTeX

Trepo, Eric; Nahon, Pierre; Bontempi, Gianluca; Valenti, Luca; Falleti, Edmondo; Nischalke, Hans Dieter; Hamza, Samia; Corradini, Stefano Ginanni; Burza, Maria Antonella; Guyot, Erwan; Donati, Benedetta; Spengler, Ulrich; Hillon, Patrick; Toniutto, Pierluigi; Henrion, Jean; Mathurin, Philippe; Moreno, Christophe; Romeo, Stefano; Deltenre, Pierre

Association between the PNPLA3 (rs738409 C>G) variant and hepatocellular carcinoma: evidence from a meta-analysis of individual participant data Miscellaneous

2013, (Conference: The Liver Meeting, American Association for the Study of Liver Diseases(2013: Washington, USA)).

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Pini, Giovanni

Towards autonomous task partitioning in swarm robotics: experiments with foraging robots PhD Thesis

2013, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

@phdthesis{info:hdl:2013/209469,
title = {Towards autonomous task partitioning in swarm robotics: experiments with foraging robots},
author = {Giovanni Pini},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/209469/1/7a3641f3-3f2f-441d-b343-77a304e6d1f9.txt},
year = {2013},
date = {2013-01-01},
abstract = {In this thesis, we propose an approach to achieve autonomous task partitioning in swarms of robots. Task partitioning is the process by which tasks are decomposed into sub-tasks and it is often an advantageous way of organizing work in groups of individuals. Therefore, it is interesting to study its application to swarm robotics, in which groups of robots are deployed to collectively carry out a mission. The capability of partitioning tasks autonomously can enhance the flexibility of swarm robotics systems because the robots can adapt the way they decompose and perform their work depending on specific environmental conditions and goals. So far, few studies have been presented on the topic of task partitioning in the context of swarm robotics. Additionally, in all the existing studies, there is no separation between the task partitioning methods and the behavior of the robots and often task partitioning relies on characteristics of the environments in which the robots operate.

This limits the applicability of these methods to the specific contexts for which they have been built. The work presented in this thesis represents the first steps towards a general framework for autonomous task partitioning in swarms of robots. We study task partitioning in foraging, since foraging abstracts practical real-world problems. The approach we propose in this thesis is therefore studied in experiments in which the goal is to achieve autonomous task partitioning in foraging. However, in the proposed approach, the task partitioning process relies upon general, task-independent concepts and we are therefore confident that it is applicable in other contexts. We identify two main capabilities that the robots should have: i) being capable of selecting whether to employ task partitioning and ii) defining the sub-tasks of a given task. We propose and study algorithms that endow a swarm of robots with these capabilities.},
note = {Funder: Universite Libre de Bruxelles},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

2012

"e, Yann-A; Bontempi, Gianluca

Prediction-Based Data Collection in Wireless Sensor Networks Book Chapter

In: CRC Press, 2012, (Language of publication: fr).

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Biggelaar, Olivier Van Den

Distributed spectrum sensing and interference management for cognitive radios with low capacity control channels PhD Thesis

2012, (Funder: Universite Libre de Bruxelles).

Abstract | Links | BibTeX

Cilia, Elisa; Vuister, Geerten W; Lenaerts, Tom

Accurate prediction of the dynamical changes within the second PDZ domain of PTP1e. Journal Article

In: PLoS computational biology, 8 (11), pp. e1002794, 2012, (DOI: 10.1371/journal.pcbi.1002794).

Abstract | Links | BibTeX

Segbroeck, Sven Van; Pacheco, J M; Lenaerts, Tom; Santos, F C

Emergence of fairness in repeated group interactions. Journal Article

In: Physical review letters, 108 (15), pp. 158104, 2012, (Language of publication: en).

Abstract | Links | BibTeX

Huculeci, Radu; Buts, Lieven; Lenaerts, Tom; van Nuland, Nico A J; Garcia-Pino, Abel

Purification, crystallization and preliminary X-ray diffraction analysis of the Fyn SH2 domain and its complex with a phosphotyrosine peptide. Journal Article

In: Acta Crystallographica. Section F: Structural Biology and Crystallization Communications Online, 68 (Pt 3), pp. 359-364, 2012, (DOI: 10.1107/S1744309112004186).

Abstract | Links | BibTeX

Haibe-Kains, Benjamin; Desmedt, Christine; Loi, Sherene; Culhane, Aedin C; Bontempi, Gianluca; Quackenbush, John; Sotiriou, Christos

A three-gene model to robustly identify breast cancer molecular subtypes. Journal Article

In: Journal of the National Cancer Institute, 104 (4), pp. 311-325, 2012, (DOI: 10.1093/jnci/djr545).

Abstract | Links | BibTeX

Vaccaro, Alfredo A; Bontempi, Gianluca; Taieb, Souhaib Ben; Villacci, Domenico D

Adaptive local learning techniques for multiple-step-ahead wind speed forecasting Journal Article

In: Electric power systems research, 83 (1), pp. 129-135, 2012, (DOI: 10.1016/j.epsr.2011.10.008).

Abstract | Links | BibTeX