Learning interpretable networks from multivariate information in biological and clinical data | Vidéo | Carmin.tv

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Learning interpretable networks from multivariate information in biological and clinical data

By Hervé Isambert

machine learning

Appears in collection : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire

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).

Information about the video

Date of recording 03/03/2020
Date of publication 23/03/2020
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19619803
Cite this video Isambert, Hervé (03/03/2020). Learning interpretable networks from multivariate information in biological and clinical data. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19619803
URL https://dx.doi.org/10.24350/CIRM.V.19619803

Domain(s)

Bibliography

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