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Bioinformatics Advance Access originally published online on January 29, 2004
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Bioinformatics 20(5) © Oxford University Press 2004; all rights reserved.

Bio-support vector machines for computational proteomics

Zheng Rong Yang 1,* and Kuo-Chen Chou 2,3

1 Department of Computer Science, Exeter University, Exeter EX4 4PT, UK, 2 Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, CA 92130, USA and 3 Tianjin Institute of Bioinformatics & Drug Discovery (TIBDD), Tianjin, China

Received on June 25, 2003 ; revised on August 7, 2003 ; accepted on August 19, 2003
Advance Access Publication January 29, 2004

Motivation: One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias.

Results: By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.

Contact: Z.R.Yang{at}exeter.ac.uk

* To whom correspondence should be addressed.


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