Predicting subcellular localization of proteins in a hybridization space | Zendy

YuDong Cai | Zendy; KuoChen Chou | Zendy

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Predicting subcellular localization of proteins in a hybridization space

Author(s) -

YuDong Cai,

KuoChen Chou

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/bth054

Subject(s) - gene ontology , jackknife resampling , computer science , computational biology , function (biology) , subcellular localization , proteomics , data mining , domain (mathematical analysis) , ontology , biology , gene , genetics , mathematics , gene expression , mathematical analysis , philosophy , statistics , epistemology , estimator

The localization of a protein in a cell is closely correlated with its biological function. With the number of sequences entering into databanks rapidly increasing, the importance of developing a powerful high-throughput tool to determine protein subcellular location has become self-evident. In view of this, the Nearest Neighbour Algorithm was developed for predicting the protein subcellular location using the strategy of hybridizing the information derived from the recent development in gene ontology with that from the functional domain composition as well as the pseudo amino acid composition.

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