A comparative study of multi-classification methods for protein fold recognition
by Ioannis K. Valavanis, George M. Spyrou, Konstantina S. Nikita
International Journal of Computational Intelligence in Bioinformatics and Systems Biology (IJCIBSB), Vol. 1, No. 3, 2010

Abstract: Fold recognition based on sequence-derived features is a complex multi-class classification problem. In the current study, we comparatively assess five different classification techniques, namely multilayer perceptron and probabilistic neural networks, nearest neighbour classifiers, multi-class support vector machines and classification trees for fold recognition on a reference set of proteins that are organised in 27 folds and are described by 125-dimensional vectors of sequence-derived features. We evaluate all classifiers in terms of total accuracy, mutual information coefficient, sensitivity and specificity measurements using a ten-fold cross-validation method. A polynomial support vector machine and a multilayer perceptron of one hidden layer of 88 nodes performed better and achieved satisfactory multi-class classification accuracies (42.8% and 42.1%, respectively) given the complexity of the problem and the reported similar classification performances of other researchers.

Online publication date: Tue, 02-Feb-2010

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