Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data

• URL: http://arxiv.org/abs/2509.02648v1
• Date: Tue, 02 Sep 2025 11:09:24 GMT
• Title: Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
• Authors: John Zobolas, Anne-Marie George, Alberto López, Sebastian Fischer, Marc Becker, Tero Aittokallio,
• Abstract summary: Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods.<n>We developed a hybrid ensemble feature selection (hEFS) approach that combines data subsampling with multiple prognostic models.<n>hEFS identifies significantly fewer and more stable biomarkers compared to the conventional, late-fusion CoxLasso models.
• Score: 4.010215468404495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods that ensure predictive performance, sparsity, and reliability for prognostic biomarker discovery. We developed a hybrid ensemble feature selection (hEFS) approach that combines data subsampling with multiple prognostic models, integrating both embedded and wrapper-based strategies for survival prediction. Omics features are ranked using a voting-theory-inspired aggregation mechanism across models and subsamples, while the optimal number of features is selected via a Pareto front, balancing predictive accuracy and model sparsity without any user-defined thresholds. When applied to multi-omics datasets from three pancreatic cancer cohorts, hEFS identifies significantly fewer and more stable biomarkers compared to the conventional, late-fusion CoxLasso models, while maintaining comparable discrimination performance. Implemented within the open-source mlr3fselect R package, hEFS offers a robust, interpretable, and clinically valuable tool for prognostic modelling and biomarker discovery in high-dimensional survival settings.

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