Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
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Bioinformatics
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ICML '05 Proceedings of the 22nd international conference on Machine learning
Short communication: Specificity rule discovery in HIV-1 protease cleavage site analysis
Computational Biology and Chemistry
Substitution matrix optimisation for peptide classification
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
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We consider the problem of classifying peptides using the information residing in their syntactic representations. This problem, which has been studied for more than a decade, has typically been investigated using distance-based metrics that involve the edit operations required in the peptide comparisons. In this paper, we shall demonstrate that the Optimal and Information Theoretic (OIT) model of Oommen and Kashyap [22] applicable for syntactic pattern recognition can be used to tackle peptide classification problem. We advocate that one can model the differences between compared strings as a mutation model consisting of random substitutions, insertions and deletions obeying the OIT model. Thus, in this paper, we show that the probability measure obtained from the OIT model can be perceived as a sequence similarity metric, using which a support vector machine (SVM)-based peptide classifier can be devised. The classifier, which we have built has been tested for eight different substitution matrices and for two different data sets, namely, the HIV-1 Protease cleavage sites and the T-cell epitopes. The results show that the OIT model performs significantly better than the one which uses a Needleman-Wunsch sequence alignment score, it is less sensitive to the substitution matrix than the other methods compared, and that when combined with a SVM, is among the best peptide classification methods available.