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dc.contributor.advisorAnderson, Dominique
dc.contributor.authorSerage, Rudolph
dc.date.accessioned2022-11-04T09:36:01Z
dc.date.available2022-11-04T09:36:01Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/11394/9408
dc.description>Magister Scientiae - MScen_US
dc.description.abstractMedical science has made substantial progress toward diagnosing, understanding the pathogenesis, and treating various causative agents of infectious disease; however, novel microbial pathogens continue to emerge, and existing pathogens continue to evolve alternative means to thrive in ever-changing environments. Various infectious disease etiological agents originate from animal reservoirs, and many have, over time, acquired the ability to cross the species barrier and alter their host range. The emergence and re-emergence of zoonotic pathogens is reported to be a consequence of changes in several factors, including ecological, behavioural, and socioeconomic variables which are arguably impossible to control. Computational methods with the capacity to evaluate large datasets, are considered invaluable tools for predicting and tracking disease outbreaks and are especially powerful when combined with machine learning techniques.en_US
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.subjectMedical scienceen_US
dc.subjectBioinformaticsen_US
dc.subjectNeural networken_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.titleA deep learning approach to predicting potential virus species crossover using convolutional neural networks and viral protein sequence patternsen_US
dc.rights.holderUniversity of the Western Capeen_US


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