English
Learning interpretable networks from multivariate information in biological and clinical data
Multi angle
Auteurs : Isambert, Hervé (Auteur de la conférence)
CIRM (Editeur )
68T05 - Learning and adaptive systems
92D10 - Genetics
CIRM (Editeur )
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Résumé :
The reconstruction of graphical models (or networks) has become ubiquitous to analyze the rapidly expanding, information-rich data of biological or clinical interest. I will outline some network reconstruction methods and applications to large scale datasets. In particular, our group has developped information-theoretic methods and machine learning tools to infer and analyze interpretable graphical models from large scale genomics data (single cell transcriptomics, tumor expression and mutation data) as well as clinical data (analysis of medical records from breast cancer patients, Institut Curie, and from elderly patients with cognitive disorders, La Pitie-Salpetriere).
Mots-Clés : machine learning
Codes MSC : Mots-Clés : machine learning
68T05 - Learning and adaptive systems
92D10 - Genetics
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de Publication : 23/03/2020 Date de Captation : 03/03/2020 Sous Collection : Research talks Catégorie arXiv : Quantitative Biology ; Computer Science Domaine(s) : Analyse Numérique & Calcul Formel ; Informatique ; Probabilités & Statistiques Format : MP4 (.mp4) - HD Durée : 01:38:23 Audience : Chercheurs Download : https://videos.cirm-math.fr/2020-03-03_Isambert.mp4 
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Informations sur la Rencontre
Nom de la Rencontre : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaireOrganisateurs de la Rencontre : Baudot, Anais ; Hubert, Florence ; Mossé, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu Dates : 02/03/2020 - 06/03/2020 Année de la rencontre : 2020 URL de la Rencontre : https://conferences.cirm-math.fr/2305.html
Données de citation
DOI : 10.24350/CIRM.V.19619803Citer cette vidéo: Isambert, Hervé (2020). Learning interpretable networks from multivariate information in biological and clinical data. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19619803 URI : http://dx.doi.org/10.24350/CIRM.V.19619803 |
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Bibliographie
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- SINGH, Param Priya, AFFELDT, Severine, MALAGUTI, Giulia, et al. Human dominant disease genes are enriched in paralogs originating from whole genome duplication. PLoS computational biology, 2014, vol. 10, no 7. - https://journals.plos.org/ploscompbiol/article/file?type=printable&id=10.1371/journal.pcbi.1003754
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