ArticleGigaScience2025
EnrichDO: a global weighted model for Disease Ontology enrichment analysis.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Leiomodin 1 deficiency promotes lipid accumulation and redirects gene regulatory programs in smooth muscle cells exposed to oxidized LDL.Research square · 2026Article
- Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery.Scientific reports · 2026Article
- A tissue microenvironment analogous to certain tumor microenvironments facilitates HIV persistence.Frontiers in immunology · 2026Article
- Article
- Ferroptosis-Related Gene CRYAB in Asthma: Bioinformatics Identification and Experimental Validation.Journal of inflammation research · 2025Article
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7 authors.
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Abstract
backgroundDisease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundreds of Gene Ontology (GO)-based enrichment analysis methods, however, DO-based methods are relatively scarce, and most current DO-based approaches are term-for-term and thus are unable to solve over-enrichment problems caused by the "true-path" rule.
resultsHere, we describe a novel double-weighted model, EnrichDO, which leverages the latest annotations of the human genome with DO terms and integrates DO graph topology on a global scale. Compared to classic enrichment methods (mainly for GO) and existing DO-based enrichment tools, EnrichDO performs better in both GO and DO enrichment analysis cases. It can accurately identify more specific terms, without ignoring the truly associated parent terms, as shown in the Alzheimer's disease (AD) case (AD ranked first). Moreover, both a simulated test and a data perturbation test validate the accuracy and robustness of EnrichDO. Finally, EnrichDO is applied to other types of datasets to expand its application, including gene expression profile datasets, a host gene set of microorganisms, and hallmark gene sets. Based on the findings reported here, EnrichDO shows significant improvement via all experimental results.
conclusionsEnrichDO provides an effective DO enrichment analysis model for gaining insight into the significance of a particular gene set in the context of disease. To increase the usability of EnrichDO, we have developed an R-based software package, which is freely available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/EnrichDO.html) or at https://github.com/liangcheng-hrbmu/EnrichDO.
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