Virtual BSC RS/BSC Life Session : Multi-omics data integration methods to study rare genetic diseases

Abstract

The technological advances and accumulation of biomedical datasets are yielding unprecedented opportunities to better understand genetic diseases, but necessitate proper exploration and integration methods to unravel a complete picture of biological systems. I will discuss about the computational strategies we recently developed, using i) multilayer networks to integrate a large range of interactions, and associated exploration algorithms and ii) dimensionality reduction to extract biological knowledge simultaneously from multiple omics. On the application side, I will discuss about the analysis of rare genetic diseases, which raise various challenges: many patients are undiagnosed, phenotypes can be highly heterogeneous, and only a few treatments exist. Selected associated publications & preprints Cantini, L., Zakeri, P., Hernandez, C., Naldi, A., Thieffry, D., Remy, E., Baudot, A., 2021. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications 12. https://doi.org/10.1038/s41467-020-20430-7 Novoa-del-Toro, E.-M., Mezura-Montes, E., Vignes, M., Magdinier, F., Tichit, L., Baudot, A., 2020. A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks. bioRxiv 2020.05.25.114215. https://doi.org/10.1101/2020.05.25.114215 Pio-Lopez, L., Valdeolivas, A., Tichit, L., Remy, É., Baudot, A., 2020. MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach. arXiv:2008.10085.