Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data

Abstract Background Accurate prediction of patient survival using high-dimensional multi-omics data requires effective feature selection methods that balance predictive performance, sparsity, and stability to enable reproducible and transparent prognostic biomarker discovery. Existing approaches oft...

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Autors principals:	John Zobolas, Anne-Marie George, Alberto López, Sebastian Fischer, Marc Becker, Tero Aittokallio
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2026-04-01
Col·lecció:	BioData Mining
Matèries:	Biomarker discovery Survival analysis Feature selection Machine learning Stability analysis High-dimensional data
Accés en línia:	https://doi.org/10.1186/s13040-026-00546-0
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