A knowledge empowered explainable gene ontology fingerprint approach to improve gene functional explication and prediction.

Abstract

Functional explication of genes is of great scientific value. However, conventional methods have challenges for those genes thatmay affect biological processes but are not annotated in public databases. Here, we developed a novel explainable gene ontology fingerprint (XGOF) method to automatically produce knowledge networks on biomedical literature in a given field which quantitatively characterizes the association between genes and ontologies. XGOF provides systematic knowledge for the potential function of genes and ontologically compares similarities and discrepancies in different disease-XGOFs integrating omics data. More importantly, XGOF can not only help to infer major cellular components in a disease microenvironment but also reveal novel gene panels or functions for in-depth experimental research where few explicit connections to diseases have previously been described in the literature. The reliability of XGOF is validated in four application scenarios, indicating a unique perspective of integrating text and data mining, with the potential to accelerate scientific discovery.