on April 28, 2017 at 2:00 pm
Inference and interrogation (knowledge extraction) of biological networks, guided by omics-type data, is currently an active research area with many applications in systems and synthetic biology. Machine learning with its wide range of techniques plays a major role, involving the development of ‘case by case’ methodologies. In this presentation, I will present tools for i) inferring gene regulatory networks including their cis-regulatory motifs; ii) estimating the activity of transcription factors and iii) extracting from functional modules. I illustrate my point with examples of ongoing projects in the team.
References:
GREAT: a web portal for Genome Regulatory Architecture Tools, Bouyioukos C, Bucchini F, Elati M, Képès F, Nucleid acids research, gkw384.
CoRegNet: reconstruction and integrated analysis of co regulatory networks R. Nicolle, F. Radvanyi, and M. Elati.
PEPPER: Cytoscape app for Protein complex Expansion using Protein -Protein intERaction networks Ch. Winterhalter, R. Nicolle, A. Louis, C. To, F. Radvanyi, and M. Elati. Bioinformatics, doi: 10.1093/btu517, 2014.
precision: prediction of CIS regulatory elements improved by gene’s positION. M. Elati, R Nicolle, I Junier, D Fernandez, R Fekih, J Font and F Képès. Nucleic Acids Research, 41 (3): 1415, 2013
Amphi Turing, Bât M3 Extension
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