Type of Document Dissertation Author ALTUN, GULSAH Author's Email Address galtun@student.gsu.edu URN etd-04202008-221608 Title MACHINE LEARNING AND GRAPH THEORY APPROACHES FOR CLASSIFICATION AND PREDICTION OF PROTEIN STRUCTURE Degree Ph.D. Department Computer Science Advisory Committee
Advisor Name Title Robert W. Harrison Committee Chair Yi Pan Committee Co-Chair Alexander Zelikovsky Committee Member Phang C. Tai Committee Member Keywords
- protein structure prediction
- feature selection
- support vector machines
- graph theory
- machine learning
- algorithm
Date of Defense 2008-03-28 Availability unrestricted Abstract Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.Files
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