Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms

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. vector machine (SVM), tertiary classifier. iv ACKNOWLEDGEMENTS I wish to take this opportunity to thank many people without whom this thesis would not have been accomplished. First and foremost, I would like to thank my thesis advisor, Dr. Yanqing Zhang. I was able to achieve this task, with his help, guidance, encouragement, and the time that he has spent on directing my thesis. I also wish to thank my thesis committee members, Dr. Saeid Belkasim and Dr. Yingshu Li, for taking time to evaluate my simulation results and to review my thesis document. I would like to express my gratitude to Dr. Hyunsoo Kim from Georgia Tech, who helped me understand the PSSM profiling. I would like to thank my fellow department members and all my friends, who patiently listen to all my doubts and queries and helped me in many ways. Last but not least, I wish to express my gratitude to my parents and my brother who have pushed me this far. I most certainly should thank my husband Ashraf, who supported me and encouraged me even when I kept bugging him. Declaration I hereby declare that, except where otherwise indicated, this document is entirely my own work and has not been submitted in whole or in part to any other university.