A New SVM-Based Decision Fusion Method Using Multiple Granular Windows for Protein Secondary Structure Prediction
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.
Binary classifier BLOSUM62 encoding scheme granular computing orthogonal profile support vector machine (SVM) tertiary Classifier
Anjum Reyaz-Ahmed Yanqing Zhang
Department of Computer Science Georgia State University Atlanta,GA 30302-3994 USA
国际会议
成都
英文
324-331
2008-05-17（万方平台首次上网日期，不代表论文的发表时间）