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

arXiv: https://arxiv.org/abs/2511.21211 · HTML: https://mlsanity.com/paper/2511.21211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Positive-Unlabelled Learning for identifying new candidate Dietary Restriction-related genes among Ageing-related genes](https://mlsanity.com/paper/2406.09898/) (2024)

## Abstract (arXiv)

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.