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

arXiv: https://arxiv.org/abs/2509.02648 · HTML: https://mlsanity.com/paper/2509.02648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 114 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 20 of 20)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework](https://mlsanity.com/paper/2302.12247/) (2023)
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- [Unbiased split variable selection for random survival forests using maximally selected rank statistics](https://mlsanity.com/paper/1605.03391/) (2016)
- [Cooperative learning for multiview analysis](https://mlsanity.com/paper/2112.12337/) (2021)
- [Large-scale benchmark study of survival prediction methods using multi-omics data](https://mlsanity.com/paper/2003.03621/) (2020)
- [The Hypervolume Indicator: Problems and Algorithms](https://mlsanity.com/paper/2005.00515/) (2020)
- [Model-free Feature Screening and FDR Control with Knockoff Features](https://mlsanity.com/paper/1908.06597/) (2019)
- [Derandomizing Knockoffs](https://mlsanity.com/paper/2012.02717/) (2020)
- [Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures](https://mlsanity.com/paper/2112.04828/) (2021)
- [Probing for sparse and fast variable selection with model-based boosting](https://mlsanity.com/paper/1702.04561/) (2017)
- [Minimum Manhattan Distance Approach to Multiple Criteria Decision Making in Multiobjective Optimization Problems](https://mlsanity.com/paper/1705.01705/) (2017)
- [A Hybrid Ensemble Feature Selection Design for Candidate Biomarkers Discovery from Transcriptome Profiles](https://mlsanity.com/paper/2108.00290/) (2021)
- [Accelerated and interpretable oblique random survival forests](https://mlsanity.com/paper/2208.01129/) (2022)
- [EFSIS: Ensemble Feature Selection Integrating Stability](https://mlsanity.com/paper/1811.07939/) (2018)
- [Distribution-free and Model-free Multivariate Feature Screening via Multivariate Rank Distance Correlation](https://mlsanity.com/paper/2110.03145/) (2021)
- [Stability Selection for Lasso, Ridge and Elastic Net Implemented with AFT Models](https://mlsanity.com/paper/1604.07311/) (2016)
- [Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection](https://mlsanity.com/paper/1802.08178/) (2018)

## Abstract (arXiv)

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.