PEDS Advance Access originally published online on August 16, 2004
Protein Engineering Design and Selection 2004 17(6):509-516; doi:10.1093/protein/gzh061
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Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition
1Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, 3Bioinformatics Research Centre, Donghau University, Shanghai 200050, 4Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, 5Tianjin Institute of Bioinformatics and Drug Discovery, Tianjin, China and 6Gordon Life Science Institute, San Diego, CA 92130, USA
2 To whom correspondence should be addressed. E-mail: mengking{at}sjtu.edu.cn; kchou{at}san.rr.com
Membrane proteins are generally classified into the following five types: (1) type I membrane proteins, (2) type II membrane proteins, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins and (5) GPI-anchored membrane proteins. Prediction of membrane protein types has become one of the growing hot topics in bioinformatics. Currently, we are facing two critical challenges in this area: first, how to take into account the extremely complicated sequence-order effects, and second, how to deal with the highly uneven sizes of the subsets in a training dataset. In this paper, stimulated by the concept of using the pseudo-amino acid composition to incorporate the sequence-order effects, the spectral analysis technique is introduced to represent the statistical sample of a protein. Based on such a framework, the weighted support vector machine (SVM) algorithm is applied. The new approach has remarkable power in dealing with the bias caused by the situation when one subset in the training dataset contains many more samples than the other. The new method is particularly useful when our focus is aimed at proteins belonging to small subsets. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, indicating that the current approach may serve as a powerful complementary tool to other existing methods for predicting the types of membrane proteins.
Received May 10, 2004; revised July 23, 2004; accepted July 26, 2004.
Edited by Alan Fersht
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