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Communication Dans Un Congrès Année : 2022

Multi-omics data integration methods: kernel and other machine learning approaches

Résumé

The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. In this presentation, I will make a brief review of popular data integration methods and then focus on kernel methods and why they are usually well suited to this task.
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hal-03856931 , version 1 (17-11-2022)

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Nathalie Vialaneix. Multi-omics data integration methods: kernel and other machine learning approaches. Machine Learning for Life Sciences, Key Initiative MUSE, Université de Montpellier, Nov 2022, Montpellier, France. ⟨hal-03856931⟩
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