Supervised multiple kernel learning approaches for multi-omics data integration

Supervised multiple kernel learning approaches for multi-omics data integration

Abstract Background Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and v...

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Bibliographic Details
Main Authors: Mitja Briscik, Gabriele Tazza, László Vidács, Marie-Agnès Dillies, Sébastien Déjean
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BioData Mining
Subjects:
Multi-omics
Data integration
Kernel methods
Deep learning
Data mining
Biomarker
Online Access:https://doi.org/10.1186/s13040-024-00406-9

Internet

https://doi.org/10.1186/s13040-024-00406-9

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