title: Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins.
abstract: In this paper, AdaBoost algorithm, a popular and effective prediction method, is applied to predict the subcellular locations of Prokaryotic and Eukaryotic Proteins-a dataset derived from SWISSPROT 33.0. Its prediction ability was evaluated by re-substitution test, Leave-One-Out Cross validation (LOOCV) and jackknife test. By comparing its results with some most popular predictors such as Discriminant Function, neural networks, and SVM, we demonstrated that the AdaBoost predictor outperformed these predictors. As a result, we arrive at the conclusion that AdaBoost algorithm could be employed as a robust method to predict subcellular location. An online web server for predicting subcellular location of prokaryotic and eukaryotic proteins is available at http://chemdata.shu.edu.cn/subcell/ .
authors.affiliation: School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Niu
authors.forename: Yu-Huan
authors.initials: YH
authors.lastname: Jin
authors.forename: Kai-Yan
authors.initials: KY
authors.lastname: Feng
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000596:Amino Acids; D016002:Discriminant Analysis; D005057:Eukaryotic Cells / Q000378:metabolism*; D020407:Internet; D042541:Intracellular Space / Q000378:metabolism*; D008954:Models, Biological; D016571:Neural Networks, Computer; D011387:Prokaryotic Cells / Q000378:metabolism*; D021381:Protein Transport; D011506:Proteins / Q000032:analysis / Q000378:metabolism*; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D011506:Proteins