PhenoLinker: Phenotype-gene link prediction and explanation using heterogeneous graph neural networks

Abstract

The association of a given human phenotype with a genetic variant remains a critical challenge in biomedical research. We present PhenoLinker, a novel graph-based system capable of associating a score to a phenotype-gene relationship by using heterogeneous information networks and a convolutional neural network-based model for graphs, which can provide an explanation for the predictions. Unlike previous approaches, PhenoLinker integrates gene and phenotype attributes, while maintaining explainability through Integrated Gradients. PhenoLinker consistently outperforms existing models in both retrospective and temporal validation tasks. This system can aid in the discovery of new associations and in understanding the consequences of human genetic variation.