GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression

Victoria Bourgeais (1) , Farida Zehraoui (1) , Blaise Hanczar (1)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes

Motivation: Medical care is becoming more and more specific to patients’ needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning, can improve the field of precision medicine. However, their use in clinics is limited as their predictions are not accompanied by an explanation. The production of accurate and intelligible predictions can benefit from the inclusion of domain knowledge. Therefore, knowledge-based deep learning models appear to be a promising solution. Results: In this paper, we propose GraphGONet, where the Gene Ontology is encapsulated in the hidden layers of a new self-explaining neural network. Each neuron in the layers represents a biological concept, combining the gene expression profile of a patient, and the information from its neighboring neurons. The experiments described in the paper confirm that our model not only performs as accurately as the state-of-the-art (non-explainable ones) but also automatically produces stable and intelligible explanations composed of the biological concepts with the highest contribution. This feature allows experts to use our tool in a medical setting.

Keywords: Deep learning; Gene expression; Gene Ontology; Precision medicine; Prior knowledge; Interpretability; Explainability

Domains: Artificial Intelligence [cs.AI]; Machine Learning [stat.ML]; Machine Learning [cs.LG]; Neural and Evolutionary Computing [cs.NE]; Bioinformatics [q-bio.QM]

Victoria Bourgeais, Farida Zehraoui, Blaise Hanczar. GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression. Bioinformatics, 2022, 38 (9), pp.2504-2511. ⟨10.1093/bioinformatics/btac147⟩. ⟨hal-03608573⟩