Welcome

30/09/2019

I yunaam a research scientist at the CIT Program (Cartes d’Identité des Tumeurs) at the French League Against Cancer.


Logotype_CIT_300dpi_CMJNRésultat de recherche d'images pour "ligue contre le cancer"

Keywords: Genomics, biostatistics, bioinformatics

Topics: multi-omics integration, network inference, signal deconvolution.


Best Poster Award #ESMO19 Barcelona

29/09/2019

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!

IMG_1854


New health data challenge: registration is open!

29/06/2019

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

Affiche Health Data Challenge 2nd Edition Sept12


First International Consensus Meeting on Pancreatic Cancer

25/06/2019

Honored to be part of this international consensus on thank you so much to Paco Real and to people !

photo_consensus

 


Data Challenge, CNRS, Aussois, December 2018

20/12/2018

We won the first prize ! Thank you to the organizers for this amazing week, such great interactions and a wonderful location !


Best investigator Award #iMig2018 May 5, 2018

07/05/2018

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.

IMG_6947.jpg


Presentation at the Mesothelioma Francophone Days, 15-16 Nov 2017

03/12/2017

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.

Screen Shot 2018-06-05 at 2.52.15 PM.png