ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
An ontology-neutral framework for enrichment analysis.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature.BMC bioinformatics · 2016Article
- An unsupervised learning method to identify reference intervals from a clinical database.Journal of biomedical informatics · 2016Article
- BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology.BMC bioinformatics · 2015Article
- Building the graph of medicine from millions of clinical narratives.Scientific data · 2014Article
- STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation.BMC bioinformatics · 2013Article
- Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs).BMC bioinformatics · 2012Article
- Markov Chain Ontology Analysis (MCOA).BMC bioinformatics · 2012Article
- Chapter 9: Analyses using disease ontologies.PLoS computational biology · 2012Article
- The gene-specific codon counting database: a genome-based catalog of one-, two-, three-, four- and five-codon combinations present in Saccharomyces cerevisiae genes.Database : the journal of biological databases and curation · 2012Article
- Mining the Gene Wiki for functional genomic knowledge.BMC genomics · 2011Article
- Enabling enrichment analysis with the Human Disease Ontology.Journal of biomedical informatics · 2011Article
- The National Center for Biomedical Ontology.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Advanced statistical methods used to analyze high-throughput data (e.g. 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 relevant for and extensible to data analysis with other high-throughput measurement modalities such as proteomics, metabolomics, and tissue-microarray assays. With the availability of tools for automatic ontology-based annotation of datasets with terms from biomedical ontologies besides the GO, we need not restrict enrichment analysis to the GO. We describe, RANSUM - Rich Annotation Summarizer - which performs enrichment analysis using any ontology in the National Center for Biomedical Ontology's (NCBO) BioPortal. We outline the methodology of enrichment analysis, the associated challenges, and discuss novel analyses enabled by RANSUM.
Indexed as
Identifiers
21347088PMC3041299What OpenQuestion holds
Registered trials
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.