XGDAG: explainable gene-disease associations via graph neural networks.

ABSTRACT:

Motivation

Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene-disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability.

Results

We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determinin

SUBMITTER: Mastropietro A 

PROVIDER: S-EPMC10421968 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature