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      • HARVEST
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      Techniques to Improve Deep Learning for Phenotype Prediction from Genotype Data

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      KOPAS-THESIS-2020.pdf (1.279Mb)
      supplementary-materials.tar.gz (13.94Mb)
      Date
      2020-11-24
      Author
      Kopas, Logan
      ORCID
      0000-0002-5525-7001
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      We 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.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Kusalik, Tony; Schneider, Dave
      Committee
      Stavness, Ian; Bett, Kirsten; Zhang, Xuekui
      Copyright Date
      August 2020
      URI
      http://hdl.handle.net/10388/13148
      Subject
      Deep learning
      bioinformatics
      genotype
      phenotype prediction
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      • Graduate Theses and Dissertations
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