In silico gene function prediction using ontology-based pattern identification | Zendy

Yingyao Zhou | Zendy; Jason A. Young | Zendy; Andrey Santrosyan | Zendy; Kaisheng Chen | Zendy; S. Frank Yan | Zendy; Elizabeth A. Winzeler | Zendy

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In silico gene function prediction using ontology-based pattern identification

Author(s) -

Yingyao Zhou,

Jason A. Young,

Andrey Santrosyan,

Kaisheng Chen,

S. Frank Yan,

Elizabeth A. Winzeler

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti111

Subject(s) - in silico , identification (biology) , computational biology , gene ontology , gene , ontology , function (biology) , data mining , computer science , kegg , biology , gene expression profiling , genome , gene expression , genetics , philosophy , botany , epistemology

With the emergence of genome-wide expression profiling data sets, the guilt by association (GBA) principle has been a cornerstone for deriving gene functional interpretations in silico. Given the limited success of traditional methods for producing clusters of genes with great amounts of functional similarity, new data-mining algorithms are required to fully exploit the potential of high-throughput genomic approaches.

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