# A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics

arXiv: https://arxiv.org/abs/2303.16914 · HTML: https://mlsanity.com/paper/2303.16914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 29 Mar 2023.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data](https://mlsanity.com/paper/1909.04169/) (2019)

## Abstract (arXiv)

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.