In the field of functional genomics, the analysis of gene expression profiles through Machine and Deep Learning is increasingly providing meaningful insight into a number of diseases. The paper proposes a novel algorithm to perform Feature Selection on genomic-scale data, which exploits the reconstruction capabilities of autoencoders and an ad-hoc defined Explainable Artificial Intelligence-based score in order to select the most informative genes for diagnosis, prognosis, and precision medicine. Results of the application on a Chronic Lymphocytic Leukemia dataset evidence the effectiveness of the algorithm, by identifying and suggesting a set of meaningful genes for further medical investigation.

翻译：在功能基因组学领域，通过机器学习和深度学习分析基因表达谱正为多种疾病提供越来越有意义的见解。本文提出一种针对基因组尺度数据的新型特征选择算法，该算法利用自编码器的重建能力与基于可解释人工智能的专用评分机制，选取对诊断、预后及精准医学最具信息量的基因。在慢性淋巴细胞白血病数据集上的应用结果表明，该算法能够有效识别并提出一组具有医学研究意义的基因，为后续临床探索提供参考。