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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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Principais autores: John Zobolas, Anne-Marie George, Alberto López, Sebastian Fischer, Marc Becker, Tero Aittokallio
Format: Artigo
Sprog:Inglês
Udgivet: BMC 2026-04-01
Serier:BioData Mining
Fag:
Biomarker discovery
Survival analysis
Feature selection
Machine learning
Stability analysis
High-dimensional data
Online adgang:https://doi.org/10.1186/s13040-026-00546-0
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