Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins.
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/ .
Niu
Bing
B
School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
Jin
Yu-Huan
YH
Feng
Kai-Yan
KY
Lu
Wen-Cong
WC
Cai
Yu-Dong
YD
Li
Guo-Zheng
GZ
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
D000596:Amino Acids; D011506:Proteins