Type of Document Master's Thesis Author Reyaz-Ahmed, Anjum B Author's Email Address areyazahmed1@student.gsu.edu URN etd-04222007-210949 Title PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES, NUERAL NETWORKS AND GENETIC ALGORITHMS Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Dr. Yanqing Zhang Committee Chair Dr. Saeid Belkasim Committee Member Dr. Yingshu Li Committee Member Keywords
- tertiary classifier.
- : Binary classifier
- BLOSUM62
- encoding scheme
- orthogonal profile
- support vector machine (SVM)
Date of Defense 2007-04-20 Availability unrestricted Abstract Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First 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. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.Files
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