Frontiers in Protein and Peptide Sciences

Volume: 1

Predicting Membrane Protein Types with Bagging Learner

Author(s): Bing Niu, Yu-Huan Yu-Huan Jin, Kai-Yan Feng, Liang Liu, Wen-Cong Lu, Yu-Dong Cai and Guo-Zheng Li

Pp: 194-205 (12)

DOI: 10.2174/9781608058624114010012

* (Excluding Mailing and Handling)


The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. Many investigators have put their efforts to the prediction of membrane protein type. Here, we propose a new approach, the bootstrap aggregating method or bragging learner, to address this problem based on the protein amino acid composition. As a demonstration, the benchmark dataset constructed by K.C. Chou and D.W. Elrod (Proteins, 1999, 34, 137-153) was used to test the new method. The overall success rate thus obtained by jackknife cross-validation was over 84%, indicating that the bragging learner as presented in this paper holds a quite high potential in predicting the attributes of proteins, or at least can play a complementary role to many existing algorithms in this area. It is anticipated that the prediction quality can be further enhanced if the pseudo amino acid composition (K.C. Chou, Proteins, 2001, 43, 246-255) can be effectively incorporated into the current predictor. An online membrane protein type prediction web server developed in our lab is available at

Keywords: Bagging, neural network, SVM, membrane protein types, jackknife cross-validation.

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