Molecular Diversity

, 12:41

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

Full Length Paper

DOI: 10.1007/s11030-008-9073-0

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


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


AdaBoost Subcellular location Self-consistency Jackknife test 

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringShanghai UniversityShanghaiChina
  2. 2.Department of Chemistry, College of SciencesShanghai UniversityShanghaiChina
  3. 3.Division of Imaging Science & Biomedical EngineeringThe University of ManchesterManchesterUK
  4. 4.Department of Combinatorics and Geometry, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
  5. 5.School of Computer Science & EngineeringShanghai UniversityShanghaiChina

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