Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques

TL;DR

This paper introduces a robust gene prioritization pipeline using Fast-mRMR feature selection to improve model simplicity, interpretability, and performance in high-dimensional biomedical data, demonstrated on Dietary Restriction genes.

Contribution

It presents a novel, efficient feature selection method that enhances gene prioritization accuracy and interpretability in biomedical datasets with high dimensionality and noise.

Findings

Significant performance improvements over existing methods.

Effective integration of heterogeneous biological features.

Enhanced model interpretability and efficiency.

Abstract

Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. However, existing methods struggle with the high dimensionality and incomplete labelling of biomedical data. This work proposes a more robust and efficient pipeline that leverages Fast-mRMR Feature Selection to retain only relevant, non-redundant features for classifiers, building simpler, more interpretable and more efficient models. Experiments in our domain of interest, prioritizing genes related to Dietary Restriction (DR), show significant improvements over existing methods and enables us to integrate heterogeneous biological feature sets for better performance, a strategy that previously degraded performance due to noise accumulation. This work focuses on DR given the availability of curated data and expert knowledge for validation, yet this pipeline would be applicable to other biological processes, proving that feature selection is critical for reliable gene prioritization in high-dimensional omics.