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

ABSTRACT:

Background

Understanding 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 p

SUBMITTER: Renaux A 

PROVIDER: S-EPMC10463539 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature