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Nam, Dougu
Bioinformatics Lab.
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WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms

Author(s)
Chi, Sang-MunNam, Dougu
Issued Date
2012-04
DOI
10.1093/bioinformatics/bts062
URI
https://scholarworks.unist.ac.kr/handle/201301/2895
Fulltext
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84859263222
Citation
BIOINFORMATICS, v.28, no.7, pp.1028 - 1030
Abstract
We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method.
Publisher
OXFORD UNIV PRESS
ISSN
1367-4803
Keyword
SEQUENCE

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