A knowledge graph approach to predict and interpret disease-causing gene interactions

BMC Bioinformatics

Research

Ilaria Tiddi¹  Michael Cochez²  Ann Nowé³  Chloé Terwagne⁴  Tom Lenaerts⁵  Alexandre Renaux⁵ 

[1] Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Discovery Lab, Elsevier, Amsterdam, The Netherlands;Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium;Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium;Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium;Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium;Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium;Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium;Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium;

关键词: Disease genetics;    Genetic interactions;    Interpretable machine-learning;    Knowledge graphs;   

DOI  :  10.1186/s12859-023-05451-5

 received in 2023-06-07, accepted in 2023-08-22,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundUnderstanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results.ResultsWe introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction.ConclusionOur method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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