Research

My work lies at the frontier of statistical machine learning, information theory and biology
In particular, my research focuses on network inference and reverse engineering of cells and genomes using statistical  logic, mutual information and networks
The ultimate goal of such research is to predict all the consequences of a genetic modification and assist in identifying the right genes to target, in order to improve cells.

Selected Publications
  1. NetBenchmark: A Bioconductor Package for Reproducible Benchmarks of Gene Regulatory Network Inference.
    Pau Bellot, Catharina Olsen, Philippe Salembier, Albert Oliveras-Verges and Patrick E. Meyer.
    BMC Bioinformatics 2015.
  2. The Rank Minrelation Coefficient.
    Patrick E. Meyer.
    Quality Technology & Quantitative Management Vol. 11. 2014. 
    Special Issue on "Graphical Causality Models: Trees, Bayesian Networks and Big Data" (ENBIS-SFdS Meeting)

  3. Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE.
    The modENCODE Consortium et al. (co-first author)
    In Science (AAAS), 2010.
    Press: Science Daily - Le Figaro.
    preversion [pdf]

  4. MINET: An open source R/Bioconductor Package for Mutual Information based Network Inference.
    Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi.
    In BMC Bioinformatics, Volume 9, 2008.

  5. Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity.
    Corrected version [pdf]
    Patrick E. Meyer, Colas Schretter and Gianluca Bontempi.
    In IEEE Journal of Selected Topics in Signal Processing (JSTSP)
    Special Issue on Genomic and Proteomic Signal Processing, Volume2, Issue 3, 2008

Other Peer-Reviewed Publications

  1. Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines
    Guillaume Lobet, Iko T. Koevoes, Manuel Noll, Patrick E. Meyer, Pierre Tocquin, Loïc Pagès, and Claire Périlleux.
    Frontiers in Plant Science, 2017
  2. Meta-Analysis Strategies for Information-Theoretic Network Inference.
    Ngoc
    C. Pham, Benjamin Haibe-Kains, Pau Bellot, Gianluca Bontempi and Patrick E. Meyer.
    In BMC BioData Mining, 2017
    Best paper award of the IEEE DEXA Workshops 2016. preversion [pdf]

  3. Study of normalization and aggregation approaches for consensus network estimation.
    Pau Bellot, Philippe Salembier, Albert Oliveras-Verges and Patrick E Meyer.
    In Proceedings of IEEE Alife 2015.preversion [pdf]

  4. Efficient combination of pairwise feature networks.
    Pau Bellot and Patrick E. Meyer, in NCW ECML2014 (JMLR workshop), Nancy, France 2014. 
    preversion [pdf]

  5. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.
    Daniel Marbach, Sushmita Roy, Ferhat Ay, Patrick E. Meyer, Rogerio Candeias, Tamer Kahveci, Christopher A. Bristow and Manolis Kellis.
    In Genome Research, 2012. 
    preversion [pdf]

  6. Estimation of temporal lags for the inference of gene regulatory networks from time series.
    Miguel Lopes, Patrick E. Meyer and Gianluca Bontempi.
    In BENELEARN'12, 2012.

  7. Causal Filter Selection in Microarray Data.
    Gianluca Bontempi and Patrick E. Meyer
    In ICML’10, International Conference On Machine Learning, 2010.
    preversion [pdf]

  8. Information-Theoretic Inference of Gene Networks Using Backward Elimination.
    Patrick E. Meyer, Daniel Marbach, Sushmita Roy and Manolis Kellis.
    In BioComp'10, International Conference on Bioinformatics and Computational Biology, 2010.
    preversion [pdf]

  9. On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information
    Catharina Olsen, Patrick E. Meyer and Gianluca Bontempi.
    In EURASIP Journal on Bioinformatics and Systems Biology, 2009

  10. Information-Theoretic Inference of Large Transcriptional Regulatory Networks
    Patrick E. Meyer, Kevin Kontos, Frederic Lafitte and Gianluca Bontempi.
    In EURASIP Journal on Bioinformatics and Systems Biology
    Special Issue on Information-Theoretic Methods for Bioinformatics, 2007 (highly accessed)

  11. A Model-Based Relevance Estimation Approach for Feature Selection in Microarray Datasets
    Gianluca Bontempi and Patrick E. Meyer.
    In ICANN'08, International Conference on Artificial Neural Networks, 2008.
    Lecture Notes in Computer Science, volume 5164, pp. 21-31, Springer, 2008

  12. Biological Network Inference Using Redundancy Analysis.
    Patrick E. Meyer, Kevin Kontos and Gianluca Bontempi.
    In BIRD'07, 1st International Conference on Bioinformatics Research and Development, 2007.
    Lecture Notes in Computer Science, volume 4414, pp. 16-27, Springer, 2007
    S. Hochreiter and R. Wagner editors.
    preversion [pdf] 

  13. On the Use of Variable Complementarity for Feature Selection in Cancer Classification.
    Patrick E. Meyer and Gianluca Bontempi.
    In EvoBIO'06, 4th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 2006.
    Lecture Notes in Computer Science, volume 3907, pp. 91-102, Springer, 2006
    F. Rothlauf et al. editors.
    preversion [pdf] 

  14. Combining Lazy Learning, Racing and Subsampling for Effective Feature Selection
    Gianluca Bontempi, Mauro Birattari and Patrick E. Meyer.
    In ICANNGA'05, 7th International Conference on Adaptive and Natural Computing Algorithms, 2005.
    Springer, pp. 393-396, 2005.
    B. Ribeiro et al. editors

  15. Speeding up Feature Selection by Using an Information Theoretic Bound
    Patrick E. Meyer, Olivier Caelen and Gianluca Bontempi.
    In BNAIC'05, 17th Belgian-Dutch Conference on Artificial Intelligence, 2005.
    KVAB, pp.166-173, 2005.
    preversion [pdf] 

  16. Collective Retrieval by Autonomous Robots
    Patrick E. Meyer.
    In STAIRS-ECAI'04, 2th Starting AI Researchers' Symposium, 2004.
    Frontiers in Artificial Intelligence and Applications, volume 109, pp. 199-204, IOS Press, 2004
    E. Onaindia and S. Staab editors.
    preversion [pdf]

Book Chapters

  1. Efficient combination of pairwise feature networks.
    Pau Bellot and Patrick E. Meyer,
    Neural Connectomics Challenge.
    Battaglia et al. Eds. Springer 2017.
  2. Information-Theoretic Gene Selection in Expression Data
    Patrick E. Meyer and Gianluca Bontempi.
    Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data.
    Elloumi et al. Editors. Wiley, 2013.
    preversion [pdf]

  3. Transcriptional Network Inference based on Information Theory.
    Patrick E. Meyer, Catharina Olsen and Gianluca Bontempi.
    Applied Statistics for Network Biology: Methods in Systems Biology. 
    Dehmer et al. Editors. Wiley, 2011. ISBN: 978-3-527-32750-8. 
    preversion [pdf]

Conferences - Workshops - Fact sheets

  • Meta-Analysis of Transcriptional Network Inference
    Patrick E. Meyer, Benjamin Haibe-Kains and Gianluca Bontempi
    Recomb Satellite 09, MIT.

  • Fact sheet: Using mutual information to infer causal relationships
    Catharina Olsen, Patrick E. Meyer and Gianluca Bontempi
    JMLR: Workshop and Conference Proceedings - NIPS 2008 workshop on causality.
    file [pdf]

Theses

  • Information-Theoretic Variable Selection and Network Inference from Microarray Data
    Ph.D. Thesis (Doctorat) in Sciences, Machine Learning Group, Universite Libre de Bruxelles, Belgium, 2008.
    file (5Mb) [pdf]

  • Feature Subset Selection in Regression with High Feature-to-Sample Ratio Datasets
    Master of Philosophy (DEA) of the Computer Science Department of the Universite Libre de Bruxelles under the supervision of Prof. Gianluca Bontempi, Belgium, 2004.

  • Experiences sur le comportement collectif et social des robots
    Master Thesis of the faculty of engineering of the Universite Libre de Bruxelles under the supervision of Prof. Marco Dorigo, with the help of Prof. Jean-Louis Deneubourg, Belgium, 2003.