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H 1 Network-guided feature selection in high-dimensional genomic data

Auteurs : Azencott, Chloé (Auteur de la Conférence)
CIRM (Editeur )

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GWAS statistical test selection stability network-guided GWAS regularization network regularization multitask

Résumé : Differences in disease predisposition or response to treatment can be explained in great part by genomic differences between individuals. This has given birth to precision medicine, where treatment is tailored to the genome of patients. This field depends on collecting considerable amounts of molecular data for large numbers of individuals, which is being enabled by thriving developments in genome sequencing and other high-throughput experimental technologies.
Unfortunately, we still lack effective methods to reliably detect, from this data, which of the genomic features determine a phenotype such as disease predisposition or response to treatment. One of the major issues is that the number of features that can be measured is large (easily reaching tens of millions) with respect to the number of samples for which they can be collected (more usually of the order of hundreds or thousands), posing both computational and statistical difficulties.
In my talk I will discuss how to use biological networks, which allow us to understand mutations in their genomic context, to address these issues. All the methods I will present share the common hypotheses that genomic regions that are involved in a given phenotype are more likely to be connected on a given biological network than not.

Keywords : bioinformatique; data integration

Codes MSC :
62P10 - Applications of statistics to biology and medical sciences
92-08 - Computational methods
92B15 - General biostatistics, See also {62P10}
92C42 - Systems biology, networks

Ressources complémentaires :
https://github.com/chagaz
https://github.com/mahito-sugiyama/Multi-SConES

    Informations sur la Vidéo

    Réalisateur : Hennenfent, Guillaume
    Langue : Anglais
    Date de publication : 23/03/2020
    Date de captation : 05/03/2020
    Collection : Research talks ; Probability and Statistics
    Format : MP4 (.mp4) - HD
    Durée : 01:27:51
    Domaine : Probability & Statistics
    Audience : Chercheurs ; Doctorants , Post - Doctorants
    Download : https://videos.cirm-math.fr/2020-03-05_Azencott.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.19620603
Cite this video as: Azencott, Chloé (2020). Network-guided feature selection in high-dimensional genomic data. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19620603
URI : http://dx.doi.org/10.24350/CIRM.V.19620603

Voir aussi

Bibliographie

  • AZENCOTT, Chloé-Agathe, GRIMM, Dominik, SUGIYAMA, Mahito, et al. Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 2013, vol. 29, no 13, p. i171-i179. - https://doi.org/10.1093/bioinformatics/btt238

  • SUGIYAMA, Mahito, AZENCOTT, Chloé-Agathe, GRIMM, Dominik, et al. Multi-task feature selection on multiple networks via maximum flows. In : Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. p. 199-207. - https://doi.org/10.1137/1.9781611973440.23



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