Research

My work lies at the frontier of statistical machine learning, information theory and biology.

More precisely, my research focuses on reverse engineering of cells and genomes using concepts such as causality, implication, information, redundancy and complementarity (or synergy) of variables in both network inference and feature selection.

Selected Publications

NetBenchmark: A Bioconductor Package for Reproducible Benchmarks of Gene Regulatory Network Inference.
1. Pau Bellot, Catharina Olsen, Philippe Salembier, Albert Oliveras-Verges and Patrick E. Meyer.
2. BMC Bioinformatics 2015.
3. The Rank Minrelation Coefficient.
4. Patrick E. Meyer.
5. Quality Technology & Quantitative Management Vol. 11. 2014.
6. Special Issue on "Graphical Causality Models: Trees, Bayesian Networks and Big Data" (ENBIS-SFdS Meeting)
7. Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE.
8. The modENCODE Consortium et al. (co-first author)
9. In Science (AAAS), 2010.
10. Press: Science Daily - Le Figaro.
11. preversion [pdf]
12. MINET: An open source R/Bioconductor Package for Mutual Information based Network Inference.
13. Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi.
14. In BMC Bioinformatics, Volume 9, 2008.
15. Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity.
16. Corrected version [pdf]
17. Patrick E. Meyer, Colas Schretter and Gianluca Bontempi.
18. In IEEE Journal of Selected Topics in Signal Processing (JSTSP)
19. Special Issue on Genomic and Proteomic Signal Processing, Volume2, Issue 3, 2008

Other Peer-Reviewed Publications

1. Gene Entity Recognition of Full Text Articles
  1. M Noll, J Lété, P Meyer
  2. ICBBS'17 Proceedings of the 6th International Conference on Bioinformatics and Biomedical Sciences, 2017
2. Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies.

- 1. JA Atkinson, G Lobet, M Noll, PE Meyer, M Griffiths, DM Wells
  2. GigaScience, 2017

Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines
1. Guillaume Lobet, Iko T. Koevoes, Manuel Noll, Patrick E. Meyer, Pierre Tocquin, Loïc Pagès, and Claire Périlleux.
2. Frontiers in Plant Science, 2017
3. Meta-Analysis Strategies for Information-Theoretic Network Inference.
4. Ngoc C. Pham, Benjamin Haibe-Kains, Pau Bellot, Gianluca Bontempi and Patrick E. Meyer.
5. In BMC BioData Mining, 2017
6. Best paper award of the IEEE DEXA Workshops 2016. preversion [pdf]
7. Study of normalization and aggregation approaches for consensus network estimation.
8. Pau Bellot, Philippe Salembier, Albert Oliveras-Verges and Patrick E Meyer.
9. In Proceedings of IEEE Alife 2015.preversion [pdf]
10. Efficient combination of pairwise feature networks.
11. Pau Bellot and Patrick E. Meyer, in NCW ECML2014 (JMLR workshop), Nancy, France 2014.
12. preversion [pdf]
13. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.
14. Daniel Marbach, Sushmita Roy, Ferhat Ay, Patrick E. Meyer, Rogerio Candeias, Tamer Kahveci, Christopher A. Bristow and Manolis Kellis.
15. In Genome Research, 2012.
16. preversion [pdf]
17. Estimation of temporal lags for the inference of gene regulatory networks from time series.
18. Miguel Lopes, Patrick E. Meyer and Gianluca Bontempi.
19. In BENELEARN'12, 2012.
20. Causal Filter Selection in Microarray Data.
21. Gianluca Bontempi and Patrick E. Meyer.
22. In ICML’10, International Conference On Machine Learning, 2010.
23. preversion [pdf]
24. Information-Theoretic Inference of Gene Networks Using Backward Elimination.
25. Patrick E. Meyer, Daniel Marbach, Sushmita Roy and Manolis Kellis.
26. In BioComp'10, International Conference on Bioinformatics and Computational Biology, 2010.
27. preversion [pdf]
28. On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information
29. Catharina Olsen, Patrick E. Meyer and Gianluca Bontempi.
30. In EURASIP Journal on Bioinformatics and Systems Biology, 2009
31. Information-Theoretic Inference of Large Transcriptional Regulatory Networks
32. Patrick E. Meyer, Kevin Kontos, Frederic Lafitte and Gianluca Bontempi.
33. In EURASIP Journal on Bioinformatics and Systems Biology
34. Special Issue on Information-Theoretic Methods for Bioinformatics, 2007 (highly accessed)
35. A Model-Based Relevance Estimation Approach for Feature Selection in Microarray Datasets
36. Gianluca Bontempi and Patrick E. Meyer.
37. In ICANN'08, International Conference on Artificial Neural Networks, 2008.
38. Lecture Notes in Computer Science, volume 5164, pp. 21-31, Springer, 2008
39. Biological Network Inference Using Redundancy Analysis.
40. Patrick E. Meyer, Kevin Kontos and Gianluca Bontempi.
41. In BIRD'07, 1st International Conference on Bioinformatics Research and Development, 2007.
42. Lecture Notes in Computer Science, volume 4414, pp. 16-27, Springer, 2007
43. S. Hochreiter and R. Wagner editors.
44. preversion [pdf]
45. On the Use of Variable Complementarity for Feature Selection in Cancer Classification.
46. Patrick E. Meyer and Gianluca Bontempi.
47. In EvoBIO'06, 4th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 2006.
48. Lecture Notes in Computer Science, volume 3907, pp. 91-102, Springer, 2006
49. F. Rothlauf et al. editors.
50. preversion [pdf]
51. Combining Lazy Learning, Racing and Subsampling for Effective Feature Selection
52. Gianluca Bontempi, Mauro Birattari and Patrick E. Meyer.
53. In ICANNGA'05, 7th International Conference on Adaptive and Natural Computing Algorithms, 2005.
54. Springer, pp. 393-396, 2005.
55. B. Ribeiro et al. editors
56. Speeding up Feature Selection by Using an Information Theoretic Bound
57. Patrick E. Meyer, Olivier Caelen and Gianluca Bontempi.
58. In BNAIC'05, 17th Belgian-Dutch Conference on Artificial Intelligence, 2005.
59. KVAB, pp.166-173, 2005.
60. preversion [pdf]
61. Collective Retrieval by Autonomous Robots
62. Patrick E. Meyer.
63. In STAIRS-ECAI'04, 2th Starting AI Researchers' Symposium, 2004.
64. Frontiers in Artificial Intelligence and Applications, volume 109, pp. 199-204, IOS Press, 2004
65. E. Onaindia and S. Staab editors.
66. preversion [pdf]

Book Chapters

1. Unsupervised GRN Ensemble.
2. Pau Bellot, Philippe Salembier, Ngoc C. Pham and Patrick E. Meyer,
3. Gene Regulatory Networks
4. Guido Sanguinetti and Vân Anh Huynh-Thu Eds. Springer 2018.
5. Efficient combination of pairwise feature networks.
6. Pau Bellot and Patrick E. Meyer,
7. Neural Connectomics Challenge.
8. Battaglia et al. Eds. Springer 2017.
9. Information-Theoretic Gene Selection in Expression Data
10. Patrick E. Meyer and Gianluca Bontempi.
11. Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data.
12. Elloumi et al. Editors. Wiley, 2013.
13. preversion [pdf]
14. Transcriptional Network Inference based on Information Theory.
15. Patrick E. Meyer, Catharina Olsen and Gianluca Bontempi.
16. Applied Statistics for Network Biology: Methods in Systems Biology.
17. Dehmer et al. Editors. Wiley, 2011. ISBN: 978-3-527-32750-8.
18. 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 and Books

- 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]

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