Techniques to Improve Deep Learning for Phenotype Prediction from Genotype Data
Date
2020-11-24
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0002-5525-7001
Type
Thesis
Degree Level
Masters
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.
Description
Keywords
Deep learning, bioinformatics, genotype, phenotype prediction
Citation
Degree
Master of Science (M.Sc.)
Department
Computer Science
Program
Computer Science