Complete List of Works

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

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},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/185849b,

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"el 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:2013/155448b,

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 = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/155448},

year  = {2013},

date = {2013-01-01},

journal = {Bioinformatics},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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

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},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/204264,

title = {Noise reduction in an adaptive multiscale local polynomial decomposition},

author = {Maarten Jansen},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/204264},

year  = {2013},

date = {2013-01-01},

journal = {European Signal Processing Conference},

note = {Language of publication: en},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/133283,

title = {Multiscale local polynomial smoothing in a lifted pyramid for non-equispaced data},

author = {Maarten Jansen},

url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/133283},

year  = {2013},

date = {2013-01-01},

journal = {IEEE transactions on signal processing},

volume = {61},

number = {3},

pages = {545-555},

note = {Language of publication: en},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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

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 = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/140580},

year  = {2013},

date = {2013-01-01},

journal = {EMBO journal},

volume = {32},

number = {5},

pages = {645-655},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/177704,

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 Fran{c c}oise 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. copyright 2013 The Authors.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/151908,

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{"e}l 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:2013/145203,

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 = {https://dipot.ulb.ac.be/dspace/bitstream/2013/145203/3/doi_129008.pdf},

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:2013/147304,

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:2013/155448,

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:2013/167761,

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

author = {Gianluca Bontempi and Souhaib Ben Taieb and Yann-A{"e}l 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. copyright 2013 Springer-Verlag.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/168524,

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. copyright 2013 Lopes and Bontempi.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/168898,

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. copyright 2013 Springer-Verlag.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:hdl:2013/183221,

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. copyright 2013 Springer-Verlag.},

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

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:2013/140580,

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 Fran{c c}ois 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:2013/297043,

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}

}