title: Prediction of protein subcellular locations using a new measure of information discrepancy.
abstract: Given a raw protein sequence, knowing its subcellular location is an important step toward understanding its function and designing further experiments. A novel method is proposed for the prediction of protein subcellular locations from sequences. For four categories of eukaryotic proteins the overall predictive accuracy is 82.0%, 2.6% higher than that by using SVM approach. For three subcellular locations of prokaryotic proteins, an overall accuracy of 89.9% is obtained. In accordance with the architecture of cells, a hierarchical prediction approach is designed. Based on amino acid composition extracellular proteins and intracellular proteins can be identified with accuracy of 97%.
authors.affiliation: Bioinformatics and Computational Biology, Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50010, USA. lixiajin@iastate.edu
authors.forename: Lixia
authors.initials: L
authors.lastname: Jin
authors.forename: Huanwen
authors.initials: H
authors.lastname: Tang
authors.forename: Weiwu
authors.initials: W
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000032:analysis / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D001426:Bacterial Proteins; D020543:Proteome