A new method to measure the semantic similarity of GO terms
Abstract
Motivation: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. Results: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. Availability: The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME Contact: jzwang@cs.clemson.edu Supplementary information: http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm
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Relatively low levels of semantic similarity were found between archaic and non-archaic enriched GO terms.
“We then computed the semantic similarity between the significantly enriched terms (GREAT reported BH-adjusted hypergeometric p ≤ 0.01) using a method that incorporates both the locations of the terms in the GO graph as well as their relations with their ancestor terms [55], and found relatively low levels of semantic similarity between archaic and non-archaic enriched GO terms (Fig 4B), suggesting”
From Denisovan introgression has shaped the immune system of present-day Papuans (2022) · cites this paper as [55]
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- Authors
- Wang, J. Z., Du, Z., Payattakool, R., Yu, P. S., & Chen, C.-F.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Corresponding author
- Wang, James Z.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-03-07
- Volume
- 23
- Issue
- 10
- Pages
- 1274-1281
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2128049108
- Cited by
- 1,377
- References
- 0
- Citations by year
- 2026: 78 · 2025: 88 · 2024: 106 · 2023: 87 · 2022: 111 · 2021: 116 · 2020: 113 · 2019: 94 · 2018: 49 · 2017: 62 · 2016: 59 · 2015: 55 · 2014: 74 · 2013: 64 · 2012: 54
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Gene Ontology, semantic similarity, Gene Ontology terms, genome annotation, semantic encoding, gene clustering, Saccharomyces Genome Database, pathway analysis, knowledge discovery
- MeSH terms
- Algorithms, Humans, Terminology as Topic, Semantics, Vocabulary, Controlled, Computational Biology, Databases, Genetic
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