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.
Domaines
Statistiques [math.ST]Origine | Fichiers produits par l'(les) auteur(s) |
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