DocumentCode
84668
Title
GRtoGR: A System for Mapping GO Relations to Gene Relations
Author
Taha, Kamal
Author_Institution
Dept. of Electr. & Comput. Eng., Khalifa Univ., Abu Dhabi, United Arab Emirates
Volume
12
Issue
4
fYear
2013
fDate
Dec. 2013
Firstpage
289
Lastpage
297
Abstract
We introduce in this paper a biological search engine called GRtoGR. Given a set S of genes, GRtoGR would determine from GO graph the most significant Lowest Common Ancestor (LCA) of the GO terms annotating the set S. This significant LCA annotates the genes that are the most semantically related to the set S. The framework of GRtoGR refines the concept of LCA by introducing the concepts of Relevant Lowest Common Ancestor (RLCA) and Semantically Relevant Lowest Common Ancestor (SRLCA). A SRLCA is the most significant LCA of the GO terms annotating the set S. We observe that the existence of the GO terms annotating the set S is dependent on the existence of this SRLCA in GO graph. That is, the terms annotating a given set of genes usually have existence dependency relationships with the SRLCA of these terms. We evaluated GRtoGR experimentally and compared it with nine other methods. Results showed marked improvement.
Keywords
bioinformatics; genetics; genomics; ontologies (artificial intelligence); search engines; GRtoGR framework; LCA annotation; biological search engine; gene ontology graph; gene relation mapping; semantically relevant lowest common ancestor; Benchmark testing; Biological processes; Gene expression; Ontologies; Search engines; Semantics; Biological search engine; GO term; Gene Ontology; related genes; related terms; semantic similarity;
fLanguage
English
Journal_Title
NanoBioscience, IEEE Transactions on
Publisher
ieee
ISSN
1536-1241
Type
jour
DOI
10.1109/TNB.2013.2278480
Filename
6579772
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