title: WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc
  
  
  BACKGROUND
  smchiks@ks.ac.kr; dougnam@unist.ac.kr
  
  
  BACKGROUND
  Supplementary data is available at http://www.btool.org/WegoLoc.
authors.affiliation: School of Computer Science and Engineering, Kyungsung University, Nam-gu, Suyoung-ro 309, Pusan, South Korea. smchiks@ks.ac.kr
authors.forename: Sang-Mun
authors.initials: SM
authors.lastname: Chi
authors.forename: Dougu
authors.initials: D
authors.lastname: Nam
mesh_terms: D000465:Algorithms; D000596:Amino Acids; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D011506:Proteins / Q000378:metabolism*; D012984:Software; D060388:Support Vector Machine; D018875:Vocabulary, Controlled
chemical_list: D000596:Amino Acids; D011506:Proteins