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Deep learning for phenotype prediction based on gene expression data
Blaize Hanczar

28 March 2019, 14h30
Salle/Bat : 465/PCRI-N
Contact :

Activités de recherche :

Résumé :
Today, an increasing effort is put in the field of Precision Medicine to better characterize patients using high resolution technologies (also known as omics) designed to profile different facets of human biology (i.e. genomics, transcriptomics, metabolomics,…). Our contribution is about the prediction of phenotype based on gene expression data with a deep neural network. We focus on two issues: the learning with a small training set and the interpretation of the network. For the small training set problem, we propose methods based on transfer learning and semi-supervised learning. For interpretation, we backpropagate the predictions through the network in order to identify relevant genes and neurons that we associate them to biological knowledge.

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