title: Support vector machine approach for protein subcellular localization prediction.
abstract: BACKGROUND
  Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.
  
  
  RESULTS
  In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals.
  
  
  BACKGROUND
  A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.
  
  
  BACKGROUND
  Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.
authors.affiliation: Institute of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, People's Republic of China. huasj00@mails.tsinghua.edu.cn
authors.forename: S
authors.initials: S
authors.lastname: Hua
authors.forename: Z
authors.initials: Z
authors.lastname: Sun
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D020407:Internet; D008954:Models, Biological; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D000596:Amino Acids; D011506:Proteins