Degree Name
MS (Master of Science)
Program
Computer and Information Science
Date of Award
12-2009
Committee Chair or Co-Chairs
Debra J. Knisley, Phillip E. Pfeiffer IV
Committee Members
Cecilia A. McIntosh, Christopher D. Wallace, Yali Liu
Abstract
Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported.
Document Type
Thesis - unrestricted
Recommended Citation
Jackson, Arthur Rhydon, "Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning." (2009). Electronic Theses and Dissertations. Paper 1820. https://dc.etsu.edu/etd/1820
Copyright
Copyright by the authors.
Included in
Amino Acids, Peptides, and Proteins Commons, Artificial Intelligence and Robotics Commons