Short communication: Specificity rule discovery in HIV-1 protease cleavage site analysis
Computational Biology and Chemistry
Short communication: Specificity rule discovery in HIV-1 protease cleavage site analysis
Computational Biology and Chemistry
Predicting HIV protease-cleavable peptides by discrete support vector machines
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Understanding protein structure prediction using SVM_DT
ISPA'05 Proceedings of the 2005 international conference on Parallel and Distributed Processing and Applications
An ensemble learning approach for prediction of phosphorylation sites
International Journal of Bioinformatics Research and Applications
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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.