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Network-guided feature selection in high-dimensional genomic data

By Chloé-Agathe Azencott

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

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.

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Citation data

  • DOI 10.24350/CIRM.V.19620603
  • Cite this video Azencott, Chloé-Agathe (05/03/2020). Network-guided feature selection in high-dimensional genomic data. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19620603
  • URL https://dx.doi.org/10.24350/CIRM.V.19620603

Bibliography

  • 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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