Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Granular Kernel Trees with parallel Genetic Algorithms for drug activity comparisons
International Journal of Data Mining and Bioinformatics
Granular support vector machines with association rules mining for protein homology prediction
Artificial Intelligence in Medicine
Applications of evolutionary SVM to prediction of membrane alpha-helices
Expert Systems with Applications: An International Journal
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Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. In this study, a new sliding window scheme is introduced with multiple granular windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. The prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. New classifier is introduced for effective tertiary classification. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.