A comparative study of multi-classification methods for protein fold recognition Online publication date: Tue, 02-Feb-2010
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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