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Correlating Expression Data with Gene Function Using Gene Ontology †
Author(s) -
Liu Qi,
Deng Yong,
Wang Chuan,
Shi TieLiu,
Li YiXue
Publication year - 2006
Publication title -
chinese journal of chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.28
H-Index - 41
eISSN - 1614-7065
pISSN - 1001-604X
DOI - 10.1002/cjoc.200690232
Subject(s) - similarity (geometry) , annotation , gene ontology , gene , gene annotation , function (biology) , computational biology , gene expression , cluster analysis , semantic similarity , microarray databases , expression (computer science) , ontology , dna microarray , data mining , genetics , biology , computer science , information retrieval , artificial intelligence , genome , image (mathematics) , programming language , philosophy , epistemology
Clustering is perhaps one of the most widely used tools for microarray data analysis. Proposed roles for genes of unknown function are inferred from clusters of genes similarity expressed across many biological conditions. However, whether function annotation by similarity metrics is reliable or not and to what extent the similarity in gene expression patterns is useful for annotation of gene functions, has not been evaluated. This paper made a comprehensive research on the correlation between the similarity of expression data and of gene functions using Gene Ontology. It has been found that although the similarity in expression patterns and the similarity in gene functions are significantly dependent on each other, this association is rather weak. In addition, among the three categories of Gene Ontology, the similarity of expression data is more useful for cellular component annotation than for biological process and molecular function. The results presented are interesting for the gene functions prediction research area.