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.

翻译：基因优先排序（识别可能与生物过程相关的基因）正日益通过人工智能技术解决。然而，现有方法在处理生物医学数据的高维性和不完整标注方面存在困难。本研究提出一种更稳健高效的流程，利用快速mRMR特征选择技术为分类器保留仅相关且非冗余的特征，从而构建更简单、更可解释且更高效的模型。在我们关注的领域——饮食限制相关基因优先排序的实验中，该方法较现有方法显示出显著改进，并使我们能够整合异质生物特征集以获得更好性能，而这一策略先前因噪声累积会导致性能下降。本研究选择饮食限制作为重点，得益于其经过整理的数据和可用于验证的专家知识，但该流程同样适用于其他生物过程，证明了特征选择对于高维组学数据中可靠基因优先排序的关键作用。