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dc.contributor.advisorKusalik, Tony
dc.contributor.advisorSchneider, Dave
dc.creatorKopas, Logan
dc.date.accessioned2020-11-24T16:33:09Z
dc.date.available2020-11-24T16:33:09Z
dc.date.created2020-08
dc.date.issued2020-11-24
dc.date.submittedAugust 2020
dc.identifier.urihttp://hdl.handle.net/10388/13148
dc.description.abstractWe show that by representing Single Nucleotide Polymorphism (SNP) data to a neural network in a way that incorporates quality scores and avoids filtering out low quality SNPs we are able to increase the effectiveness of a deep neural network for phenotype prediction from genotype in some cases. We also show that we are able to significantly increase the predictive power of a neural network by making use of transfer learning. We demonstrate these results on a Whole Genome Sequencing (WGS) Neisseria gonorrhoeae dataset where we predict Antimicrobial Resistance (AMR) as well as on an exome sequencing Lens culinaris dataset where we predict 3 growing rate phenotypes.
dc.format.mimetypeapplication/pdf
dc.subjectDeep learning
dc.subjectbioinformatics
dc.subjectgenotype
dc.subjectphenotype prediction
dc.titleTechniques to Improve Deep Learning for Phenotype Prediction from Genotype Data
dc.typeThesis
dc.date.updated2020-11-24T16:33:10Z
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Saskatchewan
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.Sc.)
dc.type.materialtext
dc.contributor.committeeMemberStavness, Ian
dc.contributor.committeeMemberBett, Kirsten
dc.contributor.committeeMemberZhang, Xuekui
dc.creator.orcid0000-0002-5525-7001


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