Techniques to Improve Deep Learning for Phenotype Prediction from Genotype Data
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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.
DegreeMaster of Science (M.Sc.)
SupervisorKusalik, Tony; Schneider, Dave
CommitteeStavness, Ian; Bett, Kirsten; Zhang, Xuekui
Copyright DateAugust 2020