{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/42113"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/42113","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Utilising machine learning techniques on simulated viral evolution datasets to improve viral recombinant identification","abstract":"This thesis explores machine learning applications for enhancing viral recombination detection. Using SANTA-SIM-generated viral evolution data, multiple computational approaches were developed and evaluated against existing methods in the Recombination Detection Program (RDP5). The study trained and tested several models, including logistic regression, gradient boosting, random forests and neural networks, on a dataset of 491 124 sequences. A novel neural network architecture employing position selection achieved the highest performance with a weighted Area Under Curve (AUC) of 0.784, surpassing RDP5's baseline AUC of 0.739. The gradient boosting classifier demonstrated strong results with an AUC of 0.765, whilst the binary neural network achieved 0.764. Performance evaluation focused on precision, recall and F1-scores to address the inherent class imbalance between recombinant and parental sequences. The models demonstrated modest performance in detecting recombinants (precision 0.627-0.687, recall 0.652-0.686). These improvements, though incremental, represent progress in automated recombination detection. The successful preliminary integration of the logistic regression model into RDP5 demonstrates the practical applicability of these approaches. This work provides a foundation for enhancing viral recombination detection through machine learning, whilst highlighting areas requiring further development to achieve more substantial improvements in detection accuracy.","abstract_html":"This thesis explores machine learning applications for enhancing viral recombination detection. Using SANTA-SIM-generated viral evolution data, multiple computational approaches were developed and evaluated against existing methods in the Recombination Detection Program (RDP5). The study trained and tested several models, including logistic regression, gradient boosting, random forests and neural networks, on a dataset of 491 124 sequences. A novel neural network architecture employing position selection achieved the highest performance with a weighted Area Under Curve (AUC) of 0.784, surpassing RDP5's baseline AUC of 0.739. The gradient boosting classifier demonstrated strong results with an AUC of 0.765, whilst the binary neural network achieved 0.764. Performance evaluation focused on precision, recall and F1-scores to address the inherent class imbalance between recombinant and parental sequences. The models demonstrated modest performance in detecting recombinants (precision 0.627-0.687, recall 0.652-0.686). These improvements, though incremental, represent progress in automated recombination detection. The successful preliminary integration of the logistic regression model into RDP5 demonstrates the practical applicability of these approaches. This work provides a foundation for enhancing viral recombination detection through machine learning, whilst highlighting areas requiring further development to achieve more substantial improvements in detection accuracy.","abstract_has_math":false,"creators":["Cullinan, Joshua"],"institution":"Computational Biology Division","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Martin, Darrin"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-22T22:23:22Z","subjects":["Medicine"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/42113","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Martin, Darrin"]},{"key":"dc:creator","label":"Author","values":["Cullinan, Joshua"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-11-06T07:22:35Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-11-06T07:22:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Computational Biology Division"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Medicine"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/42113"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis explores machine learning applications for enhancing viral recombination detection. Using SANTA-SIM-generated viral evolution data, multiple computational approaches were developed and evaluated against existing methods in the Recombination Detection Program (RDP5). The study trained and tested several models, including logistic regression, gradient boosting, random forests and neural networks, on a dataset of 491 124 sequences. A novel neural network architecture employing position selection achieved the highest performance with a weighted Area Under Curve (AUC) of 0.784, surpassing RDP5's baseline AUC of 0.739. The gradient boosting classifier demonstrated strong results with an AUC of 0.765, whilst the binary neural network achieved 0.764. Performance evaluation focused on precision, recall and F1-scores to address the inherent class imbalance between recombinant and parental sequences. The models demonstrated modest performance in detecting recombinants (precision 0.627-0.687, recall 0.652-0.686). These improvements, though incremental, represent progress in automated recombination detection. The successful preliminary integration of the logistic regression model into RDP5 demonstrates the practical applicability of these approaches. This work provides a foundation for enhancing viral recombination detection through machine learning, whilst highlighting areas requiring further development to achieve more substantial improvements in detection accuracy."]},{"key":"dc:title","label":"Title","values":["Utilising machine learning techniques on simulated viral evolution datasets to improve viral recombinant identification"]}]}],"canonical_facts":{"dc:contributor.advisor":["Martin, Darrin"],"dc:creator":["Cullinan, Joshua"],"dc:date.accessioned":["2025-11-06T07:22:35Z"],"dc:date.available":["2025-11-06T07:22:35Z"],"dc:date.issued":["2025"],"dc:description.abstract":["This thesis explores machine learning applications for enhancing viral recombination detection. Using SANTA-SIM-generated viral evolution data, multiple computational approaches were developed and evaluated against existing methods in the Recombination Detection Program (RDP5). The study trained and tested several models, including logistic regression, gradient boosting, random forests and neural networks, on a dataset of 491 124 sequences. A novel neural network architecture employing position selection achieved the highest performance with a weighted Area Under Curve (AUC) of 0.784, surpassing RDP5's baseline AUC of 0.739. The gradient boosting classifier demonstrated strong results with an AUC of 0.765, whilst the binary neural network achieved 0.764. Performance evaluation focused on precision, recall and F1-scores to address the inherent class imbalance between recombinant and parental sequences. The models demonstrated modest performance in detecting recombinants (precision 0.627-0.687, recall 0.652-0.686). These improvements, though incremental, represent progress in automated recombination detection. The successful preliminary integration of the logistic regression model into RDP5 demonstrates the practical applicability of these approaches. This work provides a foundation for enhancing viral recombination detection through machine learning, whilst highlighting areas requiring further development to achieve more substantial improvements in detection accuracy."],"dc:identifier.uri":["http://hdl.handle.net/11427/42113"],"dc:language.iso":["en"],"dc:publisher.department":["Computational Biology Division"],"dc:subject":["Medicine"],"dc:title":["Utilising machine learning techniques on simulated viral evolution datasets to improve viral recombinant identification"],"dc:type":["Thesis / Dissertation"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:22Z"}