ArticleBioinformatics (Oxford, England)2010
Identifying informative subsets of the Gene Ontology with information bottleneck methods.
Article in Bioinformatics (Oxford, England), 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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Who cites it
12 citing papers in PubMed.
- A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets.PloS one · 2018Article
- Using predictive specificity to determine when gene set analysis is biologically meaningful.Nucleic acids research · 2017Article
- Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.Cancer research · 2016Article
- Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets.PLoS computational biology · 2015Article
- Article
- A semi-automated methodology for finding lipid-related GO terms.Database : the journal of biological databases and curation · 2014Article
- Semantic particularity measure for functional characterization of gene sets using gene ontology.PloS one · 2014Article
- Conceptualization of molecular findings by mining gene annotations.BMC proceedings · 2013Article
- Article
- Article
- Quality of computationally inferred gene ontology annotations.PLoS computational biology · 2012Article
- Integrating genome and functional genomics data to reveal perturbed signaling pathways in ovarian cancers.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2012Article
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2 authors.
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Abstract
motivationThe Gene Ontology (GO) is a controlled vocabulary designed to represent the biological concepts pertaining to gene products. This study investigates the methods for identifying informative subsets of GO terms in an automatic and objective fashion. This task in turn requires addressing the following issues: how to represent the semantic context of GO terms, what metrics are suitable for measuring the semantic differences between terms, how to identify an informative subset that retains as much as possible of the original semantic information of GO.
resultsWe represented the semantic context of a GO term using the word-usage-profile associated with the term, which enables one to measure the semantic differences between terms based on the differences in their semantic contexts. We further employed the information bottleneck methods to automatically identify subsets of GO terms that retain as much as possible of the semantic information in an annotation database. The automatically retrieved informative subsets align well with an expert-picked GO slim subset, cover important concepts and proteins, and enhance literature-based GO annotation. AVAILABILITY: http://carcweb.musc.edu/TextminingProjects/.
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