Techniques to Improve Deep Learning for Phenotype Prediction from Genotype Data
| dc.contributor.advisor | Kusalik, Tony | |
| dc.contributor.advisor | Schneider, Dave | |
| dc.contributor.committeeMember | Stavness, Ian | |
| dc.contributor.committeeMember | Bett, Kirsten | |
| dc.contributor.committeeMember | Zhang, Xuekui | |
| dc.creator | Kopas, Logan | |
| dc.creator.orcid | 0000-0002-5525-7001 | |
| dc.date.accessioned | 2020-11-24T16:33:09Z | |
| dc.date.available | 2020-11-24T16:33:09Z | |
| dc.date.created | 2020-08 | |
| dc.date.issued | 2020-11-24 | |
| dc.date.submitted | August 2020 | |
| dc.date.updated | 2020-11-24T16:33:10Z | |
| dc.description.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. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10388/13148 | |
| dc.subject | Deep learning | |
| dc.subject | bioinformatics | |
| dc.subject | genotype | |
| dc.subject | phenotype prediction | |
| dc.title | Techniques to Improve Deep Learning for Phenotype Prediction from Genotype Data | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Computer Science | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Saskatchewan | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.Sc.) |
Files
License bundle
1 - 1 of 1