Genomic Selection with Deep Neural Networks

dc.contributor.advisor David Grant
dc.contributor.advisor William Beavis
dc.contributor.author Mcdowell, Riley
dc.contributor.department Agronomy
dc.date 2018-08-11T06:52:44.000
dc.date.accessioned 2020-06-30T03:07:47Z
dc.date.available 2020-06-30T03:07:47Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2001-01-01
dc.date.issued 2016-01-01
dc.description.abstract <p>Reduced costs for DNA marker technology has generated a huge amount of molecular</p> <p>data and made it economically feasible to generate dense genome-wide marker maps of lines</p> <p>in a breeding program. Increased data density and volume has driven an exploration of</p> <p>tools and techniques to analyze these data for cultivar improvement. Data science theory</p> <p>and application has experienced a resurgence of research into techniques to detect or ”learn”</p> <p>patterns in noisy data in a variety of technical applications. Several variants of machine</p> <p>learning have been proposed for analyzing large DNA marker data sets to aid in pheno-</p> <p>type prediction and genomic selection. Here, we present a review of the genomic prediction</p> <p>and machine learning literature. We apply deep learning techniques from machine learn-</p> <p>ing research to six phenotypic prediction tasks using published reference datasets. Because</p> <p>regularization frequently improves neural network prediction accuracy, we included regular-</p> <p>ization methods in the neural network models. The neural network models are compared to</p> <p>a selection of regularized Bayesian and linear regression techniques commonly employed for</p> <p>phenotypic prediction and genomic selection. On three of the phenotype prediction tasks,</p> <p>regularized neural networks were the most accurate of the models evaluated. Surprisingly,</p> <p>for these data sets the depth of the network architecture did not affect the accuracy of the</p> <p>trained model. We also find that concerns about the computer processing time needed to</p> <p>train neural network models to perform well in genomic prediction tasks may not apply when</p> <p>Graphics Processing Units are used for model training.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/15973/
dc.identifier.articleid 6980
dc.identifier.contextkey 11169449
dc.identifier.doi https://doi.org/10.31274/etd-180810-5600
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/15973
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/30156
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/15973/0-genomic_neuralnet_master.zip|||Fri Jan 14 20:49:21 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/etd/15973/1-genomic_neuralnet_paper_master.zip|||Fri Jan 14 20:49:21 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/etd/15973/McDowell_iastate_0097M_16108.pdf|||Fri Jan 14 20:49:27 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Computer Sciences
dc.subject.disciplines Plant Sciences
dc.subject.keywords Data Science
dc.subject.keywords Genomic Selection
dc.subject.keywords Neural Networks
dc.subject.keywords Plant Breeding
dc.supplemental.bitstream genomic_neuralnet_master.zip
dc.supplemental.bitstream genomic_neuralnet_paper_master.zip
dc.title Genomic Selection with Deep Neural Networks
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication fdd5c06c-bdbe-469c-a38e-51e664fece7a
thesis.degree.discipline Plant Breeding
thesis.degree.level thesis
thesis.degree.name Master of Science
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