Computational Network Biology - Machine Learning

University of Lille
Job Type:
  • Postdoctoral ((2 +1 years))
Degree Level Required:
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Computational Network Biology - Machine Learning

The I3-BioNet concept is particularly illustrative and interesting and paves the way to a research which goes beyond the simple analysis of biological data, by targeting the biological system in its entirety.We combined ML software, with network biology software to execute a cycle of systems biology model development. AI systems have super-human scientific powers: they can learn from vast amounts of data, execute error-free logical reasoning, execute near optimal probabilistic reasoning, coordinate the parallel testing of different hypotheses; and only scientists have the knowledge and experience to direct these experiments into profitable areas of research.

For further information on the topic, see:

  • A. Coutant, K. Roper, D. Trejo-Banos, D. Bouthinon, M. Carpenter, J. Grzebyta, G. Santini, H. Soldano, M. Elati, J. Ramon, C. Rouveirol, L. Soldatova, R. D. King. Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. PNAS,116 (36) 18142-18147, 2020.
  • Zerrouk, N., Miagoux, Q., Dispot, A., Elati, M., & Niarakis, A. (2020). Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference. Scientific reports, 10(1), 1-13.
  • W. Dhifli, J. Puig, M. Elati. Latent network-based representations for large-scale gene expression data analysis. BMC Bioinformatics, 2019.
  • P. Trébulle, J-M Nicaud, Ch. Leplat, M. Elati, Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica NPJ Systems Biology and Applications (2017): Aug 11;3:21. eCollection 2017.
  • R. Nicolle, F. Radvanyi, and M. Elati, CoRegNet: reconstruction and integrated analysis of co-regulatory networks, Bioinformatics, btv305, 2015.
  • Elati M, Neuvial P, Bolotin-Fukuhara M, Barillot E, Radvanyi F, Rouveirol C. LICORN: learning cooperative regulation networks from gene expression data. Bioinformatics 2007; 23:2407-14.


We combined ML software (for data analysis, model formation, experimental design), with network biology software (bioinformatics, systems biology modelling) to execute a cycle of systems biology model development. A second challenge is the integration of explanations and visualization into the AI system to facilitate the collaboration with scientists and accelerate systems biology models development.


Candidates should have a background or strong interest in computational network biology and machine learning and solid programming skills (e.g., R/Python).

  • How to Apply

    Interested candidates are encouraged to submit a CV, contact details of two references and a short statement of research interest electronically to Prof. Mohamed Elati (

Additional Information

This post-doc position is funded by ANR as part of the FNR INTER/ANR PRCI GREENER project. To be assured of full consideration, applications must arrive by February 28, 2021. Please feel free to contact us for informal inquiries and additional information.