Predicting gene function through systematic analysis and quality assessment of high-throughput data | Zendy

Patrick Kemmeren | Zendy; Thessa T. J. P. Kockelkorn | Zendy; Theo Bijma | Zendy; Rogier Donders | Zendy; Frank C. P. Holstege | Zendy

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Predicting gene function through systematic analysis and quality assessment of high-throughput data

Author(s) -

Patrick Kemmeren,

Thessa T. J. P. Kockelkorn,

Theo Bijma,

Rogier Donders,

Frank C. P. Holstege

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/bti103

Subject(s) - computer science , scalability , throughput , data mining , function (biology) , computational biology , genomics , genome , functional genomics , gene , biology , genetics , database , telecommunications , wireless

Determining gene function is an important challenge arising from the availability of whole genome sequences. Until recently, approaches based on sequence homology were the only high-throughput method for predicting gene function. Use of high-throughput generated experimental data sets for determining gene function has been limited for several reasons.

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