ArticleiScience2023
A knowledge empowered explainable gene ontology fingerprint approach to improve gene functional explication and prediction.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An automatic NGS feature extraction algorithm for predicting EBV-associated nasopharyngeal cancer and high-risk mutation.Virology journal · 2025Article
- Decoding hepatobiliary-specific immune gene patterns in gastrointestinal cancers via gene ontology fingerprints, multi-omics, and experimental integration.Precision clinical medicine · 2025Article
- Novel biomarker genes for the prediction of post-hepatectomy survival of patients with NAFLD-related hepatocellular carcinoma.Cancer cell international · 2023Article
- Impact of apolipoprotein A1 on tumor immune microenvironment, clinical prognosis and genomic landscape in hepatocellular carcinoma.Precision clinical medicine · 2023Article
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Authors and funding
14 authors.
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Abstract
Functional explication of genes is of great scientific value. However, conventional methods have challenges for those genes that may 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.
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