I am a research scientist at the CIT Program (Cartes d’Identité des Tumeurs) at the French League Against Cancer.
Keywords: Genomics, biostatistics, bioinformatics
Topics: multi-omics integration, network inference, signal deconvolution.
I am a research scientist at the CIT Program (Cartes d’Identité des Tumeurs) at the French League Against Cancer.
Keywords: Genomics, biostatistics, bioinformatics
Topics: multi-omics integration, network inference, signal deconvolution.
Our project COMETH is dedicated to big data integration in Health. We are currently developing a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification. Want to know more?
Deconbench: our first benchmarking platform
European coordinators:
Thanks again to all the participants for the great week and the intensive collaborative work! Hope to see everyone for a next edition of the challenge #hadaca2019 @programmeCIT @laliguecancer @TIMC_Lab @grenobledata @EIThealth_FR
Honored to receive this prize! I presented our recent results on Cancer heterogeneity quantification using a deconvolution approach and its clinical impacts. Thank you #ESMO19 and thank you to all my collaborators!
We are organizing together with the BMC TIMC-IMAG team – CNRS, the 2nd edition of an international health data challenge on statistical methods to quantify the cell populations in a tumor using omics data, one of the main current challenges in cancer genomics! Join us for this very exciting event ! More info https://tinyurl.com/hadaca2019
Honored to be part of this international consensus on #PancreaticCancer thank you so much to Paco Real and to #MSKCC people !
During this International Conference, I presented our collaborative work with Didier Jean’s team (Functional genomics of solid tumor, Inserm Unit U1162, Hôpital Saint-Louis, Paris) about MPM heterogeneity characterization using molecular gradients.
I presented a collaborative work with Didier Jean’s team (Functional genomics of solid tumor, Inserm Unit U1162, Hôpital Saint-Louis, Paris) about MPM heterogeneity characterization using molecular gradients. In our study, we show using a deconvolution approach, that molecular gradients bring new light on intra-tumor heterogeneity of MPM, leading to a reconsideration of existant MPM molecular classifications. We further show that this new way of thinking the pathology provides a significant contribution to clinical applications with implications in prognosis and therapeutic strategies, including immunotherapies and targeted therapies.
Scientific Paper: http://www.cell.com/cell-reports/abstract/S2211-1247(17)31606-6
In brief (french):