Mohamed Elati
Professeur des universités - Bio-Informatique et biologie des systèmes, intelligence artificielle
CNU : SECTION 27 - INFORMATIQUE
- Laboratoire / équipe
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Composantes, facultés
- UFR DES SCIENCES DE SANTE ET DU SPORT
- FACULTE DE PHARMACIE
- UFR DES SCIENCES DE SANTE ET DU SPORT
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Domaines de recherche
Machine Learning, Bioinformatics, computational network biology, Systems pharmacology, Automation of Scientific Research

Mohamed Elati
Professeur des universités - Bio-Informatique et biologie des systèmes, intelligence artificielle
Axes de recherche
Funded Projects
Europe :
- 2021-2023 : GREENER: Gene and Regulatory Elements Networks Involved in Rice Root Tissue Differentiation. - PRCI ANR/FNR - Coordinators Dr. Christophe Perin (CIRAD, Montpellier), Role : PI Univ. Lille-CANTHER (own funding 195K€).
- 2015-18: Adaptive « Automated Scientific Laboratory - AdaLab » - CHIST-ERA - Coordinators Prof. Larisa Soldatova (Brunel University), Prof. Ross King (Univ. of Manchester) – 1300 K€. Role : ANR coordinateur, PI UEVE-iSSB (198K€).
- 2012-15: “A system biology approach to dissect cilia function and its disruption in human genetic disease” - SYSCILIA (EC FP7 - Health Cooperation programme). Coordinator Dr. Ronald Roepman (Univ of Nigmegen) – €1200K. Role: PI UEVE-iSSB (200K€)
National :
- 2018-22: INTEGRIN: Systems biology of integrin inhibition-induced apoptosis for novel glioblastoma treatment, INCa PLBIO, Grant N2017-145. Role PI Univ. Lille-CANTHER (own funding: 115 K€)
- 2015-19: LIONs: Large-scale Integrative approach to unravel the relationships between differentiatiON and tumorigenesiS. ITMO cancer/INSERM 2015 - coordinator: Mohamed Elati 750 K€. Role: coordinator PI Univ Lille-CANTHER (220K€)
- 2016-19: CHASSY: From multi-scale modeling of biological network to engineering metabolic circuits in a biotechnology chassis, Paris-Saclay IDEX, Grant Programme Interdisciplinaire IDI (co-PI), Horizon 2020 No 720824
- 2014-16: « Ingénierie robuste et évolution dirigée de voies métaboliques synthétiques par intégration des approches génomique » coordinator. Dr. François Jean-Marie (INSA Toulouse) – 500€K Rôle: co- PI François Képès (170 K€).
- 2013-14: “Comparaison de Réseaux de régulation par Enumération de PErturbations - CREPE (PEPS CNRS). Coordinator Pr. Etienne Birmelé (Univ. Paris 5) - 20 K€. Rôle: PI UEVE-iSSB (8K€)
- 2011-13: “Search for new therapeutic targets through the Identification of Networks Specifically altered during tumorIGenesis” - INSIGht (INCa). Coordinator Dr. François Radvanyi (Institut Curie) – €464K. Rôle: PI UEVE-iSSB (115 K€)
CANTHER-XAI platform and software packages.

We have developed a large set of tools that serve as building blocks for the CANTHER-XAI platform. These tools are frequently accessed and downloaded and are increasingly being recognized and cited worldwide. They also present a major asset for the creation of a complete software ecosystem in computational network biology that ensures easy to access and that can be used in a transparent manner by any life scientist, regardless of computing expertise.
- CoRegFlux: R Bioconductor package (Trejo el al., BMC Systems Biology2017, Coutant et al., PNAS 2019). Integrating transcriptional activity in genome-scale models of metabolism.
- LatNet : R package (Dhifli el al., BMC Bioinformatics 2018). Latent
- network-based representations for large-scale gene expression data analysis.
- CoRegNet: is an R bioconductor package, which enables learning of gene regulatory networks from transcriptome data and infers master regulators controlling the transition between phenotypes. (Download stats: https://bioconductor.org/packages/stats/bioc/CoRegNet/).
- PEPPER: is a plugin cytoscape, which enables finding pathways connecting a protein set within a PPI-network using multi-objective optimization. Published in 2014 (Download stats: apps.cytoscape.org/download/stats/pepper/)
- GREAT : The Genome REgulatory and Architecture Tools (GREAT). GREAT is a software suite of related and interconnected tools, currently able to perform systematic analyses of genome regularities (GREAT-Scan-Pattern) as well as improve TFBS prediction based on gene position information (GREATScan- PreCisIon).