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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書誌詳細

主要な著者:	John Zobolas, Anne-Marie George, Alberto López, Sebastian Fischer, Marc Becker, Tero Aittokallio
フォーマット:	Artigo
言語:	Inglês
出版事項:	BMC 2026-04-01
シリーズ:	BioData Mining
主題:	Biomarker discovery Survival analysis Feature selection Machine learning Stability analysis High-dimensional data
オンライン･アクセス:	https://doi.org/10.1186/s13040-026-00546-0
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