Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning.

<p>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.</p>

Name thesis:degree_name

MS (Master of Science)

Level thesis:degree_level

Thesis - unrestricted

Discipline thesis:degree_discipline

Computer and Information Science

Year dc:date.issued

2009

Author dc:creator

• Jackson, Arthur Rhydon

• UGT
• machine learning
• graph
• flavonoid
• bayesian
• protein
• Amino Acids, Peptides, and Proteins
• Artificial Intelligence and Robotics
• Chemicals and Drugs
• Computer Sciences
• Medicine and Health Sciences
• Physical Sciences and Mathematics

Statement dc:rights

• Copyright by the authors.

Repository record dc:identifier

https://dc.etsu.edu/etd/1820

OAI identifier oai:identifier

oai:dc.etsu.edu:etd-3172