Using AdaBoost for the Prediction of Subcellular Location of Prokaryotic and Eukaryotic Proteins

Overview

Journal Mol Divers

Date 2008 May 29

PMID 18506593

Citations 19

Authors

Bing Niu

Yu-Huan Jin

Kai-Yan Feng

Wen-Cong Lu

Yu-Dong Cai

Guo-Zheng Li

Affiliations

Soon will be listed here.

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/ .

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