Molecular Diversity

, 12:41

Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins

Authors

  • Bing Niu
    • School of Materials Science and EngineeringShanghai University
  • Yu-Huan Jin
    • Department of Chemistry, College of SciencesShanghai University
  • Kai-Yan Feng
    • Division of Imaging Science & Biomedical EngineeringThe University of Manchester
    • School of Materials Science and EngineeringShanghai University
    • Department of Chemistry, College of SciencesShanghai University
    • Department of Chemistry, College of SciencesShanghai University
    • Department of Combinatorics and Geometry, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological SciencesChinese Academy of Sciences
  • Guo-Zheng Li
    • School of Computer Science & EngineeringShanghai University
Full Length Paper

DOI: 10.1007/s11030-008-9073-0

Cite this article as:
Niu, B., Jin, Y., Feng, K. et al. Mol Divers (2008) 12: 41. doi:10.1007/s11030-008-9073-0

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/.

Keywords

AdaBoostSubcellular locationSelf-consistencyJackknife test

Copyright information

© Springer Science+Business Media B.V. 2008