m

F Nous contacter


0
     
Multi angle

H 1 Learning interpretable networks from multivariate information in biological and clinical data

Auteurs : Isambert, Hervé (Auteur de la Conférence)
CIRM (Editeur )

    Loading the player...

    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).

    Keywords : machine learning

    Codes MSC :
    68T05 - Learning and adaptive systems
    92D10 - Genetics

      Informations sur la Vidéo

      Réalisateur : Hennenfent, Guillaume
      Langue : Anglais
      Date de publication : 23/03/2020
      Date de captation : 03/03/2020
      Collection : Research talks ; Numerical Analysis and Scientific Computing ; Computer Science ; Probability and Statistics
      Format : MP4
      Durée : 01:38:23
      Domaine : Numerical Analysis & Scientific Computing ; Computer Science ; Probability & Statistics
      Audience : Chercheurs ; Doctorants , Post - Doctorants
      Download : https://videos.cirm-math.fr/2020-03-03_Isambert.mp4

    Informations sur la rencontre

    Nom de la rencontre : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire
    Organisateurs 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 Congrès : https://conferences.cirm-math.fr/2305.html

    Citation Data

    DOI : 10.24350/CIRM.V.19619803
    Cite this video as: 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


    Voir aussi

    Bibliographie

    1. 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

    2. 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

    3. 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

    4. 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

    5. 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

    6. 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

    7. 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

    8. 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

    9. 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

Imagette Video

Z