Virtual BSC RS/BSC Life Session : Multi-omics data integration methods to study rare genetic diseases
Year: 2021
Type: conference-paper
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.
Author: Baudot, Anais
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7115092108