Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914470409797632 |
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| author | Fernández-Farelo, Rubén Paz-Ruza, Jorge Guijarro-Berdiñas, Bertha Alonso-Betanzos, Amparo Freitas, Alex A. |
| author_facet | Fernández-Farelo, Rubén Paz-Ruza, Jorge Guijarro-Berdiñas, Bertha Alonso-Betanzos, Amparo Freitas, Alex A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques Fernández-Farelo, Rubén Paz-Ruza, Jorge Guijarro-Berdiñas, Bertha Alonso-Betanzos, Amparo Freitas, Alex A. Machine Learning 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. |
| title | Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.21211 |