The nature of statistical learning theory
The nature of statistical learning theory
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A parallel mixture of SVMs for very large scale problems
Neural Computation
The Journal of Machine Learning Research
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
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Cysteines may form covalent bonds, known as disulfide bridges, that have an important role in stabilizing the native conformation of proteins. Several methods have been proposed for predicting the bonding state of cysteines, either using local context or using global protein descriptors. In this paper we introduce an SVM based predictor that operates in two stages. The first stage is a multi-class classifier that operates at the protein level, using either standard Gaussian or spectrum kernels. The second stage is a binary classifier that refines the prediction by exploiting local context enriched with evolutionary information in the form of multiple alignment profiles. At both stages, we enriched profile encoding with information about cysteine conservation. The prediction accuracy of the system is 85% measured by 5-fold cross validation, on a set of 716 proteins from the September 2001 PDB Select dataset.