MS (Master of Science)
Computer and Information Science
Date of Award
Committee Chair or Co-Chairs
Debra J. Knisley, Phillip E. Pfeiffer IV
Cecilia A. McIntosh, Christopher D. Wallace, Yali Liu
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.
Thesis - Open Access
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 by the authors.