ArticleJournal of biomedical informatics2011
Enabling enrichment analysis with the Human Disease Ontology.
Article in Journal of biomedical informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Ontology development and use for cholangiocarcinoma risk factors and predictions: a term enrichment data analysis and machine learning classification.Journal of biomedical semantics · 2026Article
- An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.Scientific data · 2025Article
- Article
- SVM-DO: identification of tumor-discriminating mRNA signatures via support vector machines supported by Disease Ontology.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
- Pancreatlas: Applying an Adaptable Framework to Map the Human Pancreas in Health and Disease.Patterns (New York, N.Y.) · 2020Article
- Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Predicting the need for a reduced drug dose, at first prescription.Scientific reports · 2018Article
- IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.Scientific reports · 2018Article
- Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- An unsupervised learning method to identify reference intervals from a clinical database.Journal of biomedical informatics · 2016Article
- Statin Intensity or Achieved LDL? Practice-based Evidence for the Evaluation of New Cholesterol Treatment Guidelines.PloS one · 2016Article
- In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics.BMC complementary and alternative medicine · 2015Article
- Developing a molecular roadmap of drug-food interactions.PLoS computational biology · 2015Article
- Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data.Nucleic acids research · 2015Article
- Thematic series on biomedical ontologies in JBMS: challenges and new directions.Journal of biomedical semantics · 2014Review
- Evaluation of research in biomedical ontologies.Briefings in bioinformatics · 2013Article
- Enrichment analysis applied to disease prognosis.Journal of biomedical semantics · 2013Article
- Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs).BMC bioinformatics · 2012Article
- Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.Bioinformatics (Oxford, England) · 2012Article
- Using ontology-based annotation to profile disease research.Journal of the American Medical Informatics Association : JAMIA · 2012Article
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3 authors.
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Abstract
Advanced statistical methods used to analyze high-throughput data such as gene-expression assays result in long lists of "significant genes." One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene set, and is widely used to make sense of the results of high-throughput experiments. Our goal is to develop and apply general enrichment analysis methods to profile other sets of interest, such as patient cohorts from the electronic medical record, using a variety of ontologies including SNOMED CT, MedDRA, RxNorm, and others. Although it is possible to perform enrichment analysis using ontologies other than the GO, a key pre-requisite is the availability of a background set of annotations to enable the enrichment calculation. In the case of the GO, this background set is provided by the Gene Ontology Annotations. In the current work, we describe: (i) a general method that uses hand-curated GO annotations as a starting point for creating background datasets for enrichment analysis using other ontologies; and (ii) a gene-disease background annotation set - that enables disease-based enrichment - to demonstrate feasibility of our method.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.