• Citer

A knowledge graph approach for interpretable prediction of pathogenic genetic interactions

par Renaux, Alexandre ;Terwagne, Chloé CT;Cochez, Michael;Tiddi, Ilaria;Nowé, Ann;Lenaerts, Tom
Référence European Conference on Computational Biology (ECCB) 2022 (2022-07: Sitges, Spain)
Publication Publié, 2022-11-23

Poster de conférence

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

An increasing number of clinical studies are reporting patterns of oligogenic inheritance in genetic diseases. Despite the advent of methods able to predict the pathogenicity of variant combinations, the underlying biological mechanisms remain unknown, since these models offer limited interpretability. To advance towards a better understanding of oligogenic disease aetiology, we developed a new interpretable predictive method based on a knowledge graph. This heterogenous network integrates curated oligogenic combinations together with multiple biological networks and biomedical ontologies. Our approach successfully captures association rules solely based on multi-hop relationships between genes. It combines them as a decision set model which can predict the pathogenicity of new gene pairs. These predictions come with explanations, obtained by querying the knowledge graph, which highlight relevant paths. The benchmarking of this model in a cross-validation setting achieves high accuracy and recalls independent gene pairs from recently published digenic combinations. The analysis of the rule-based paths highlights relevant contributors to the disease and shows the ability of this approach to generate knowledge-based hypotheses to investigate new disease mechanisms.