Complete List of Works

@article{info:hdl:2013/222877d,

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 = {https://dipot.ulb.ac.be/dspace/bitstream/2013/222877/5/doi_206504.pdf},

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}

}

@article{info:hdl:2013/220673d,

title = {The bias–variance decomposition in profiled attacks},

author = {Liran Lerman and Gianluca Bontempi and Olivier Markowitch},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/220673},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Cryptographic Engineering},

volume = {5},

number = {4},

pages = {255-267},

abstract = {The profiled attacks challenge the security of cryptographic devices in the worst case scenario. We elucidate the reasons underlying the success of different profiled attacks (that depend essentially on the context) based on the well-known bias–variance tradeoff developed in the machine learning field. Note that our approach can easily be extended to non-profiled attacks. We show (1) how to decompose (in three additive components) the error rate of an attack based on the bias–variance decomposition, and (2) how to reduce the error rate of a model based on the bias–variance diagnostic. Intuitively, we show that different models having the same error rate require different strategies (according to the bias–variance decomposition) to reduce their errors. More precisely, the success rate of a strategy depends on several criteria such as its complexity, the leakage information and the number of points per trace. As a result, a suboptimal strategy in a specific context can lead the adversary to overestimate the security level of the cryptographic device. Our results also bring warnings related to the estimation of the success rate of a profiled attack that can lead the evaluator to underestimate the security level. In brief, certify that a chip leaks (or not) sensitive information represents a hard if not impossible task.},

note = {DOI: 10.1007/s13389-015-0106-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/223764d,

title = {Template attacks vs Machine Learning Revisited},

author = {Liran Lerman and Romain Poussier and Gianluca Bontempi and Olivier Markowitch and Franccois-Xavier Standaert},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/223764},

year  = {2015},

date = {2015-01-01},

journal = {Lecture notes in computer science},

volume = {9064},

pages = {20-33},

note = {Language of publication: en},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/205371d,

title = {A machine learning approach against a masked AES: Reaching the limit of side-channel attacks with a learning model},

author = {Liran Lerman and Gianluca Bontempi and Olivier Markowitch},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/205371},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Cryptographic Engineering},

volume = {5},

number = {2},

pages = {123-139},

abstract = {Side-channel attacks challenge the security of cryptographic devices. A widespread countermeasure against these attacks is the masking approach. Masking combines sensitive variables with secret random values to reduce its leakage. In 2012, Nassar et al. (DATE, pp 1173–1178. IEEE, 2012) presented a new lightweight (low-cost) boolean masking countermeasure to protect the implementation of the Advanced Encryption Standard (AES) block-cipher. This masking scheme represents the target algorithm of the DPAContest V4 (http://www.dpacontest.org/home/, 2013). In this paper, we present the first machine learning attack against a specific masking countermeasure (more precisely the low-entropy boolean masking countermeasure of Nassar et al.), using the dataset of the DPAContest V4. We succeeded to extract each targeted byte of the key of the masked AES with 7.8 traces during the attacking phase with a strategy based solely on machine learning models. Finally, we compared our proposal with (1) a stochastic attack, (2) a strategy based on template attack and (3) a multivariate regression attack. We show that an attack based on a machine learning model reduces significantly the number of traces required during the attacking step compared to these profiling attacks when analyzing the same leakage information.},

note = {DOI: 10.1007/s13389-014-0089-3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/184045c,

title = {A wavelet-enhanced inversion method for water quality retrieval from high spectral resolution data for complex waters},

author = {Eva Ampe and Okke Batelaan and Dries Raymaekers and Els Knaeps and Erin E. L. Hestir and Maarten Jansen},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/184045},

year  = {2015},

date = {2015-01-01},

journal = {IEEE transactions on geoscience and remote sensing},

volume = {53},

number = {2},

pages = {869-882},

abstract = {Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection. © 2014 IEEE.},

note = {DOI: 10.1109/TGRS.2014.2330251},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/205439d,

title = {Using shRNA experiments to validate gene regulatory networks},

author = {Catharina Olsen and Kathleen Fleming and Niall Prendergast and Renee Rubio and Frank Emmert-Streib and Gianluca Bontempi and John Quackenbush and Benjamin Haibe-Kains},

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/205439/4/doi_189066.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Genomics Data},

volume = {4},

pages = {123-126},

abstract = {Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field.In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.},

note = {DOI: 10.1016/j.gdata.2015.03.011},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/177704d,

title = {Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients},

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

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/177704/4/doi_161331.pdf},

year  = {2013},

date = {2013-01-01},

journal = {Genomics Data},

volume = {1},

pages = {7-10},

abstract = {Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant Trial of Principle (TOP) study, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II-alpha (TOP2A) and develop a gene expression signature to identify those patients who do not benefit from anthracyclines. Here we describe in details the contents and quality controls for the gene expression and clinical data associated with the study published by Desmedt and colleagues in the Journal of Clinical Oncology in 2011 (Desmedt et al., 2011). We also provide R code to easily access the data and perform the quality controls and basic analyses relevant to this dataset. © 2013 The Authors.},

note = {DOI: 10.1016/j.gdata.2013.09.001},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/147304d,

title = {Semi-Supervised Template Attack},

author = {Liran Lerman and Stéphane Fernandes Medeiros and Nikita Veshchikov and Cédric Meuter and Gianluca Bontempi and Olivier Markowitch},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/147304},

year  = {2013},

date = {2013-01-01},

journal = {Lecture Notes in Computer Science},

volume = {7864},

pages = {184-199},

abstract = {Side channel attacks take advantage of information leakagesin cryptographic devices. Template attacks form a family of side channelattacks which is reputed to be extremely effective. This kind of attacksassumes that the attacker fully controls a cryptographic device before at-tacking a similar one. In this paper, we propose to relax this assumption bygeneralizing the template attack using a method based on a semi-supervisedlearning strategy. The effectiveness of our proposal is confirmed by softwaresimulations, by experiments on a 8-bit microcontroller and by a comparisonto a template attack as well as to two supervised machine learning methods.},

note = {DOI: 10.1007/978-3-642-40026-1_12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/155448d,

title = {mRMRe: an R package for parallelized mRMR ensemble feature selection},

author = {Nicolas De Jay and Simon Papillon-Cavanagh and Catharina Olsen and N. El-Hachem and Gianluca Bontempi and Benjamin Haibe-Kains},

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/155448/3/doi_140339.pdf},

year  = {2013},

date = {2013-01-01},

journal = {Bioinformatics},

abstract = {Motivation: Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach in order to better explore the feature space and build more robust predictors. To deal with the computational complexity of the ensemble approach the main functions of the package are implemented and parallelized in C using the openMP API.Results: Our ensemble mRMR implementations outperform the classical mRMR approach in terms of prediction accuracy. They identify genes more relevant to the biological context and may lead to richer biological interpretations. The parallelized functions included in the package show significant gains in terms of run-time speed when compared to previously released packages.Availability: The R package mRMRe is available on CRAN and is provided open source under the Artistic-2.0 License. The code used to generate all the results reported in this application note is available from Supplementary File 1.Contact: bhaibeka@ircm.qc.caSupplementary Information: Supplementary information is available at Bioinformatics online.},

note = {DOI: 10.1093/bioinformatics/btt383},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/167761d,

title = {Machine learning strategies for time series forecasting},

author = {Gianluca Bontempi and Souhaib Ben Taieb and Yann-Aël Le Borgne},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/167761},

year  = {2013},

date = {2013-01-01},

journal = {Lecture Notes in Business Information Processing},

volume = {138 LNBIP},

pages = {62-77},

abstract = {The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting. © 2013 Springer-Verlag.},

note = {DOI: 10.1007/978-3-642-36318-4_3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/168898d,

title = {Racing for unbalanced methods selection},

author = {Andrea Dal Pozzolo and Olivier Caelen and Serge Waterschoot and Gianluca Bontempi},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/168898},

year  = {2013},

date = {2013-01-01},

journal = {Lecture notes in computer science},

volume = {8206 LNCS},

pages = {24-31},

abstract = {State-of-the-art classification algorithms suffer when the data is skewed towards one class. This led to the development of a number of techniques to cope with unbalanced data. However, as confirmed by our experimental comparison, no technique appears to work consistently better in all conditions. We propose to use a racing method to select adaptively the most appropriate strategy for a given unbalanced task. The results show that racing is able to adapt the choice of the strategy to the specific nature of the unbalanced problem and to select rapidly the most appropriate strategy without compromising the accuracy. © 2013 Springer-Verlag.},

note = {DOI: 10.1007/978-3-642-41278-3_4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/155962,

title = {Good agreements make good friends.},

author = {The Anh T A H Han and Lu{'i}s Moniz Pereira and Francisco C Santos and Tom Lenaerts},

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/155962/4/doi_140875.pdf},

year  = {2013},

date = {2013-01-01},

journal = {Scientific reports},

volume = {3},

pages = {2695},

abstract = {When starting a new collaborative endeavor, it pays to establish upfront how strongly your partner commits to the common goal and what compensation can be expected in case the collaboration is violated. Diverse examples in biological and social contexts have demonstrated the pervasiveness of making prior agreements on posterior compensations, suggesting that this behavior could have been shaped by natural selection. Here, we analyze the evolutionary relevance of such a commitment strategy and relate it to the costly punishment strategy, where no prior agreements are made. We show that when the cost of arranging a commitment deal lies within certain limits, substantial levels of cooperation can be achieved. Moreover, these levels are higher than that achieved by simple costly punishment, especially when one insists on sharing the arrangement cost. Not only do we show that good agreements make good friends, agreements based on shared costs result in even better outcomes.},

note = {DOI: 10.1038/srep02695},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/138049,

title = {On the dynamics of neutral mutations in a mathematical model for a homogeneous stem cell population.},

author = {Arne Traulsen and Tom Lenaerts and Jorge J M Pacheco and David Dingli},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/138049},

year  = {2013},

date = {2013-01-01},

journal = {Journal of the Royal Society, Interface / the Royal Society},

volume = {10},

number = {79},

pages = {20120810},

abstract = {The theory of the clonal origin of cancer states that a tumour arises from one cell that acquires mutation(s) leading to the malignant phenotype. It is the current belief that many of these mutations give a fitness advantage to the mutant population allowing it to expand, eventually leading to disease. However, mutations that lead to such a clonal expansion need not give a fitness advantage and may in fact be neutral-or almost neutral-with respect to fitness. Such mutant clones can be eliminated or expand stochastically, leading to a malignant phenotype (disease). Mutations in haematopoietic stem cells give rise to diseases such as chronic myeloid leukaemia (CML) and paroxysmal nocturnal haemoglobinuria (PNH). Although neutral drift often leads to clonal extinction, disease is still possible, and in this case, it has important implications both for the incidence of disease and for therapy, as it may be more difficult to eliminate neutral mutations with therapy. We illustrate the consequences of such dynamics, using CML and PNH as examples. These considerations have implications for many other tumours as well.},

note = {DOI: 10.1098/rsif.2012.0810},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/140580c,

title = {TET2 and TET3 regulate GlcNAcylation and H3K4 methylation through OGT and SET1/COMPASS.},

author = {Rachel Deplus and Benjamin Delatte and Marie K Schwinn and Matthieu Defrance and Jacqui Méndez and Nancy Murphy and Mark A Dawson and Michael Volkmar and Pascale Putmans and Emilie Calonne and Alan H Shih and Ross L Levine and Olivier Bernard and Thomas Mercher and Eric Solary and Marjeta Urh and Danette Daniels and Franccois Fuks},

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/140580/4/140580.pdf},

year  = {2013},

date = {2013-01-01},

journal = {EMBO journal},

volume = {32},

number = {5},

pages = {645-655},

abstract = {TET proteins convert 5-methylcytosine to 5-hydroxymethylcytosine, an emerging dynamic epigenetic state of DNA that can influence transcription. Evidence has linked TET1 function to epigenetic repression complexes, yet mechanistic information, especially for the TET2 and TET3 proteins, remains limited. Here, we show a direct interaction of TET2 and TET3 with O-GlcNAc transferase (OGT). OGT does not appear to influence hmC activity, rather TET2 and TET3 promote OGT activity. TET2/3-OGT co-localize on chromatin at active promoters enriched for H3K4me3 and reduction of either TET2/3 or OGT activity results in a direct decrease in H3K4me3 and concomitant decreased transcription. Further, we show that Host Cell Factor 1 (HCF1), a component of the H3K4 methyltransferase SET1/COMPASS complex, is a specific GlcNAcylation target of TET2/3-OGT, and modification of HCF1 is important for the integrity of SET1/COMPASS. Additionally, we find both TET proteins and OGT activity promote binding of the SET1/COMPASS H3K4 methyltransferase, SETD1A, to chromatin. Finally, studies in Tet2 knockout mouse bone marrow tissue extend and support the data as decreases are observed of global GlcNAcylation and also of H3K4me3, notably at several key regulators of haematopoiesis. Together, our results unveil a step-wise model, involving TET-OGT interactions, promotion of GlcNAcylation, and influence on H3K4me3 via SET1/COMPASS, highlighting a novel means by which TETs may induce transcriptional activation.},

note = {DOI: 10.1038/emboj.2012.357},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/151908b,

title = {Management and interpretation of medical data related to cerebral pasly: the ICT4 Rehab project},

author = {Bruno Bonnechere and Vanessa Wermenbol and Bernard Dan and Patrick Salvia and Yann-A"el Le Borgne and Gianluca Bontempi and Stijn Vansummeren and Victor Sholukha and Fedor Moiseev and Bart Jansen and Marcel Rooze and Serge Van Sint Jan},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/151908},

year  = {2013},

date = {2013-01-01},

journal = {European journal of paediatric neurology},

volume = {17},

number = {1},

pages = {32},

note = {Language of publication: na},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/185849,

title = {Recherche intégrée relative `a l'appareil musculosquelettique : application `a la prise en charge clinique de l'infirmité motrice cérébrale (IMC) -- le projet ICT4Rehab},

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

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/185849},

year  = {2013},

date = {2013-01-01},

journal = {Médecine},

volume = {29},

number = {5},

pages = {529-536},

note = {Language of publication: na},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/186425d,

title = {From protein sequence to dynamics and disorder with DynaMine.},

author = {Elisa Cilia and Rita Pancsa and Peter Tompa and Tom Lenaerts and Wim Vranken},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/186425},

year  = {2013},

date = {2013-01-01},

journal = {Nature communications},

volume = {4},

pages = {2741},

abstract = {Protein function and dynamics are closely related; however, accurate dynamics information is difficult to obtain. Here based on a carefully assembled data set derived from experimental data for proteins in solution, we quantify backbone dynamics properties on the amino-acid level and develop DynaMine–a fast, high-quality predictor of protein backbone dynamics. DynaMine uses only protein sequence information as input and shows great potential in distinguishing regions of different structural organization, such as folded domains, disordered linkers, molten globules and pre-structured binding motifs of different sizes. It also identifies disordered regions within proteins with an accuracy comparable to the most sophisticated existing predictors, without depending on prior disorder knowledge or three-dimensional structural information. DynaMine provides molecular biologists with an important new method that grasps the dynamical characteristics of any protein of interest, as we show here for human p53 and E1A from human adenovirus 5.},

note = {DOI: 10.1038/ncomms3741},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/145203c,

title = {Comparison and validation of genomic predictors for anticancer drug sensitivity.},

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

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/145203},

year  = {2013},

date = {2013-01-01},

journal = {Journal of the American Medical Informatics Association},

volume = {20},

number = {4},

pages = {597-602},

abstract = {An enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel.},

note = {DOI: 10.1136/amiajnl-2012-001442},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/147304c,

title = {Semi-Supervised Template Attack},

author = {Liran Lerman and Stéphane Fernandes Medeiros and Nikita Veshchikov and Cédric Meuter and Gianluca Bontempi and Olivier Markowitch},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/147304},

year  = {2013},

date = {2013-01-01},

journal = {Lecture Notes in Computer Science},

volume = {7864},

pages = {184-199},

abstract = {Side channel attacks take advantage of information leakagesin cryptographic devices. Template attacks form a family of side channelattacks which is reputed to be extremely effective. This kind of attacksassumes that the attacker fully controls a cryptographic device before at-tacking a similar one. In this paper, we propose to relax this assumption bygeneralizing the template attack using a method based on a semi-supervisedlearning strategy. The effectiveness of our proposal is confirmed by softwaresimulations, by experiments on a 8-bit microcontroller and by a comparisonto a template attack as well as to two supervised machine learning methods.},

note = {DOI: 10.1007/978-3-642-40026-1_12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/168524c,

title = {Experimental assessment of static and dynamic algorithms for gene regulation inference from time series expression data},

author = {Miguel Lopes and Gianluca Bontempi},

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/168524/3/doi_152154.pdf},

year  = {2013},

date = {2013-01-01},

journal = {Frontiers in Genetics},

volume = {4},

number = {DEC},

abstract = {Accurate inference of causal gene regulatory networks from gene expression data is an open bioinformatics challenge. Gene interactions are dynamical processes and consequently we can expect that the effect of any regulation action occurs after a certain temporal lag. However such lag is unknown a priori and temporal aspects require specific inference algorithms. In this paper we aim to assess the impact of taking into consideration temporal aspects on the final accuracy of the inference procedure. In particular we will compare the accuracy of static algorithms, where no dynamic aspect is considered, to that of fixed lag and adaptive lag algorithms in three inference tasks from microarray expression data. Experimental results show that network inference algorithms that take dynamics into account perform consistently better than static ones, once the considered lags are properly chosen. However, no individual algorithm stands out in all three inference tasks, and the challenging nature of network inference tasks is evidenced, as a large number of the assessed algorithms does not perform better than random. © 2013 Lopes and Bontempi.},

note = {DOI: 10.3389/fgene.2013.00303},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/183221c,

title = {A time series approach for profiling attack},

author = {Liran Lerman and Olivier Markowitch and Gianluca Bontempi and Souhaib Ben Taieb},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/183221},

year  = {2013},

date = {2013-01-01},

journal = {Lecture notes in computer science},

volume = {8204},

pages = {75-94},

abstract = {The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack is reputed as the strongest power analysis attack, they effectiveness is strongly dependent on the validity of the Gaussian assumption. This led recently to the appearance of nonparametric approaches, often based on machine learning strategies. Though these approaches outperform template attack, they tend to neglect the potential source of information available in the temporal dependencies between power values. In this paper, we propose an original multi-class profiling attack that takes into account the temporal dependence of power traces. The experimental study shows that the time series analysis approach is competitive and often better than static classification alternatives. © 2013 Springer-Verlag.},

note = {DOI: 10.1007/978-3-642-41224-0_7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/297043,

title = {Why is it so hard to say sorry?},

author = {The Anh T A H Han and Lu{'i}s Marcelo Pereira and Francisco C Santos and Tom Lenaerts},

url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/297043/3/6744-30548-1-PB.pdf},

year  = {2013},

date = {2013-01-01},

journal = {BNAIC},

pages = {322-323},

abstract = {When making a mistake, individuals are willing to apologize to secure further cooperation, even if the apology is costly. Similarly, individuals arrange commitments to guarantee that an action such as a cooperative one is in the others' best interest, and thus will be carried out to avoid eventual penalties for commitment failure. Hence, both apology and commitment should go side by side in behavioral evolution. Here we discuss our work published in [6], wherein we study the relevance of a combination of those two strategies in the context of the iterated Prisoner's Dilemma (IPD). We show that apologizing acts are rare in non-committed interactions, especially whenever cooperation is very costly, and that arranging prior commitments can considerably increase the frequency of such behavior. In addition, we show that with or without commitments, apology resolves conflicts only if it is sincere, i.e. costly enough. Most interestingly, our model predicts that individuals tend to use much costlier apology in committed relationships than otherwise, because it helps better identify free-riders such as fake committers.},

note = {Language of publication: en},

keywords = {},

pubstate = {published},

tppubtype = {article}

}