A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics
This work addresses feature selection in genomics for diagnosis and precision medicine, but it appears incremental as it builds on existing deep learning and XAI methods.
The paper tackles the problem of selecting informative genes from genomic data for medical applications by proposing a novel algorithm that combines autoencoders and an Explainable AI-based score, achieving effectiveness in identifying meaningful genes for Chronic Lymphocytic Leukemia.
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.