PRIME, Multi-team interdisciplinary research projects (https://miti.cnrs.fr/projets-recherche-interdisciplinaire-multi-equipe-prime/)
ML4RegGen, Machine Learning for Regulatory Genomics (https://www.lirmm.fr/ml4reggen/)
Characterizing the cis-regulatory code of DNA, i.e. the genomic grammar that regulates gene expression, is an area of intense research, with numerous applications in genetics and cancerology. Recently, several statistical learning and deep learning approaches have shown that it is possible to predict gene expression on the basis of DNA sequence alone. However, the vast majority of these models are not very interpretable, and do not allow us to set up a reverse engineering process capable of identifying the genomic elements (motifs and sequences) responsible for this regulation. The aim of our transdisciplinary research group is to propose new learning models that are both predictive and interpretable.
Links : https://miti.cnrs.fr/prime/ml4reggen/
https://www.lirmm.fr/ml4reggen/
Contacte : brehelin@lirmm.fr
