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

00:00:00 / 00:00:00

Learning interpretable networks from multivariate information in biological and clinical data

De Hervé Isambert

machine learning

Apparaît dans la 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).

Informations sur la vidéo

Date de captation 03/03/2020
Date de publication 23/03/2020
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.19619803
Citer cette vidéo 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

Domaine(s)

Bibliographie

SELLA, Nadir, VERNY, Louis, UGUZZONI, Guido, et al. MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data. Bioinformatics, 2018, vol. 34, no 13, p. 2311-2313. - https://doi.org/10.1093/bioinformatics/btx844
VERNY, Louis, SELLA, Nadir, AFFELDT, Séverine, et al. Learning causal networks with latent variables from multivariate information in genomic data. PLoS computational biology, 2017, vol. 13, no 10, p. e1005662. - https://doi.org/10.1371/journal.pcbi.1005662
EVLAMPIEV, Kirill et ISAMBERT, Hervé. Conservation and topology of protein interaction networks under duplication-divergence evolution. Proceedings of the National Academy of Sciences, 2008, vol. 105, no 29, p. 9863-9868. - https://doi.org/10.1073/pnas.0804119105
SINGH, Param Priya, AFFELDT, Séverine, CASCONE, Ilaria, et al. On the expansion of “dangerous” gene repertoires by whole-genome duplications in early vertebrates. Cell reports, 2012, vol. 2, no 5, p. 1387-1398. - https://doi.org/10.1016/j.celrep.2012.09.034
SINGH, Param Priya, ARORA, Jatin, et ISAMBERT, Hervé. Identification of ohnolog genes originating from whole genome duplication in early vertebrates, based on synteny comparison across multiple genomes. PLoS computational biology, 2015, vol. 11, no 7. - https://dx.doi.org/10.1371%2Fjournal.pcbi.1004394
MAKINO, Takashi et MCLYSAGHT, Aoife. Ohnologs in the human genome are dosage balanced and frequently associated with disease. Proceedings of the National Academy of Sciences, 2010, vol. 107, no 20, p. 9270-9274. - https://doi.org/10.1073/pnas.0914697107
HUMINIECKI, Lukasz et HELDIN, Carl Henrik. 2R and remodeling of vertebrate signal transduction engine. BMC biology, 2010, vol. 8, no 1, p. 146. - https://doi.org/10.1186/1741-7007-8-146
SINGH, Param Priya, AFFELDT, Séverine, CASCONE, Ilaria, et al. On the expansion of “dangerous” gene repertoires by whole-genome duplications in early vertebrates. Cell reports, 2012, vol. 2, no 5, p. 1387-1398. - https://doi.org/10.1016/j.celrep.2012.09.034
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

Codes MSC

Dernières questions liées sur MathOverflow

Pour poser une question, votre compte Carmin.tv doit être connecté à mathoverflow

Poser une question sur MathOverflow

Copyright Carmin.tv 2025

Donner son avis