Title :
GRtoGR: A System for Mapping GO Relations to Gene Relations
Author_Institution :
Dept. of Electr. & Comput. Eng., Khalifa Univ., Abu Dhabi, United Arab Emirates
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;
Journal_Title :
NanoBioscience, IEEE Transactions on
DOI :
10.1109/TNB.2013.2278480