Machine learning for genomics

mars
28

jeudi 28 de 14h30 à 17h30

Laboratoire de recherche en informatique (LRI) - Bâtiment 650 - Rue Noetzlin - 91400 Orsay
Bâtiment 650 - Rue Noetzlin - 91400 Orsay
Gif-sur-Yvette
France
Gif-sur-Yvette

Le développement de méthodes statistiques d'analyse de données capables d’épouser les structures globales et spécifiques est essentiel pour qui souhaite extraire de l'information pertinente.

Le Groupe de Travail (GT) PASADENA financé par DigiCosme organise en partenariat avec les équipes BioInformatique LRI et LIX ses prochains séminaires les jeudis 14 et 28 mars 2019 au LRI - Bâtiment 650 - Salle 465 - 91400 Orsay cedex France Programme Jeudi 14 mars - 14h30 Intervenant : Romain Menegaux (MINES ParisTech/ Institut Curie) Title : Continuous embeddings of DNA sequencing reads, and application to metagenomics Résumé : We propose a new model for fast classification of DNA sequences output by next generation sequencing machines. The model, which we call fastDNA, embeds DNA sequences in a vector space by learning continuous low-dimensional representations of the k-mers it contains. We show on metagenomics benchmarks that it outperforms state-of-the-art methods in terms of accuracy and scalability. Jeudi 28 mars - 14h30 Intervenant : Blaize Hanczar (IBISC / U Evry) Title: Deep learning for phenotype prediction based on gene expression data 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.

Source: openagenda.com


Laboratoire de recherche en informatique (LRI) - Bâtiment 650 - Rue Noetzlin - 91400 Orsay
Bâtiment 650 - Rue Noetzlin - 91400 Orsay
Gif-sur-Yvette
France


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