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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Jackson, Arthur Rhydon"],"institution":null,"degree_name":"MS (Master of Science)","degree_level":"Thesis - unrestricted","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-12-19T08:00:00Z","date_published":"2009-12-19T08:00:00Z","updated_at":"2026-07-24T02:20:55Z","subjects":["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"],"languages":[],"rights":["Copyright by the authors."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.etsu.edu/etd/1820","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Jackson, Arthur Rhydon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2009-12-19T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - unrestricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS (Master of Science)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Copyright by the authors."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.etsu.edu/context/etd/article/3172/viewcontent/JacksonA112109a.pdf","https://dc.etsu.edu/etd/1820"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning."]}]}],"canonical_facts":{"dc:creator":["Jackson, Arthur Rhydon"],"dc:date.issued":["2009-12-19T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://dc.etsu.edu/context/etd/article/3172/viewcontent/JacksonA112109a.pdf","https://dc.etsu.edu/etd/1820"],"dc:rights":["Copyright by the authors."],"dc:subject":["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"],"dc:title":["Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning."],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis - unrestricted"],"thesis:degree_name":["MS (Master of Science)"]},"updated_at":"2026-07-24T02:20:55Z"}