Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Application Domains

Analysis of genomic data

For many years now, select collaborates with Marie-Laure Martin-Magniette (URGV) for the analysis of genomic data. An important theme of this collaboration is using statistically sound model-based clustering methods to discover groups of co-expressed genes from microarray and high-throughput sequencing data. In particular, identifying biological entities that share similar profiles across several treatment conditions, such as co-expressed genes, may help identify groups of genes that are involved in the same biological processes.

Yann Vasseur has completed a thesis co-supervised by Gilles Celeux and Marie-Laure Martin-Magniette on this topic, which is also an interesting investigation domain for the latent block model developed by select . For this work, Yann Vasseur dealt with high-dimensional ill-posed problems where the number of variable was almost equal to the number of observations. He designed heuristic tools using regularized regression methods to circumvent this difficulty.

select collaborates with Anavaj Sakuntabhai and Philippe Dussart (Pasteur Institute) on predicting dengue severity using only low-dimensional clinical data obtained at hospital arrival. An ongoing project also involves statistical meta-analysis of newly collected dengue gene expression data along with recently published data sets from other groups. Further collaborations are underway in dengue fever and encephalitis with researchers at the Pasteur Institute.

select collaborates with Inserm/Paris-Saclay researchers at Kremlin-Bicêtre hospital on cyclic transcriptional clocks and renal corticosteroid signaling, developing statistical tests for synchronous signals.

select is involved in the ANR “jeunes chercheurs” MixStatSeq directed by Cathy Maugis (INSA Toulouse), which is concerned with statistical analysis and clustering of RNASeq genomics data.