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Nam, Dougu
Statistical Genomics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms

Cited 12 times inthomson ciCited 12 times inthomson ci
Title
WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms
Author
Chi, Sang-MunNam, Dougu
Keywords
SEQUENCE
Issue Date
201204
Publisher
OXFORD UNIV PRESS
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.
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DOI
http://dx.doi.org/10.1093/bioinformatics/bts062
ISSN
1367-4803
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