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Genomic selection with deep neural n...

McDowell, Riley.

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Genomic selection with deep neural networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Genomic selection with deep neural networks./
Author:	McDowell, Riley.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	49 p.
Notes:	Source: Masters Abstracts International, Volume: 56-03.
Contained By:	Masters Abstracts International56-03(E).
Subject:	Plant sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10243302
ISBN:	9781369528244

Genomic selection with deep neural networks.
McDowell, Riley.

Genomic selection with deep neural networks. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 49 p.

Source: Masters Abstracts International, Volume: 56-03.

Thesis (M.S.)--Iowa State University, 2016.

Reduced costs for DNA marker technology has generated a huge amount of molecular data and made it economically feasible to generate dense genome-wide marker maps of lines in a breeding program. Increased data density and volume has driven an exploration of tools and techniques to analyze these data for cultivar improvement. Data science theory and application has experienced a resurgence of research into techniques to detect or "learn" patterns in noisy data in a variety of technical applications. Several variants of machine learning have been proposed for analyzing large DNA marker data sets to aid in phenotype prediction and genomic selection. Here, we present a review of the genomic prediction and machine learning literature. We apply deep learning techniques from machine learning research to six phenotypic prediction tasks using published reference datasets. Because regularization frequently improves neural network prediction accuracy, we included regularization methods in the neural network models. The neural network models are compared to a selection of regularized Bayesian and linear regression techniques commonly employed for phenotypic prediction and genomic selection. On three of the phenotype prediction tasks, regularized neural networks were the most accurate of the models evaluated. Surprisingly, for these data sets the depth of the network architecture did not affect the accuracy of the trained model. We also find that concerns about the computer processing time needed to train neural network models to perform well in genomic prediction tasks may not apply when Graphics Processing Units are used for model training.

ISBN: 9781369528244Subjects--Topical Terms:

3173832
Plant sciences.

Genomic selection with deep neural networks.
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300 $a 49 p.
500 $a Source: Masters Abstracts International, Volume: 56-03.
500 $a Includes supplementary digital materials.
500 $a Advisers: David Grant; William Beavis.
502 $a Thesis (M.S.)--Iowa State University, 2016.
520 $a Reduced costs for DNA marker technology has generated a huge amount of molecular data and made it economically feasible to generate dense genome-wide marker maps of lines in a breeding program. Increased data density and volume has driven an exploration of tools and techniques to analyze these data for cultivar improvement. Data science theory and application has experienced a resurgence of research into techniques to detect or "learn" patterns in noisy data in a variety of technical applications. Several variants of machine learning have been proposed for analyzing large DNA marker data sets to aid in phenotype prediction and genomic selection. Here, we present a review of the genomic prediction and machine learning literature. We apply deep learning techniques from machine learning research to six phenotypic prediction tasks using published reference datasets. Because regularization frequently improves neural network prediction accuracy, we included regularization methods in the neural network models. The neural network models are compared to a selection of regularized Bayesian and linear regression techniques commonly employed for phenotypic prediction and genomic selection. On three of the phenotype prediction tasks, regularized neural networks were the most accurate of the models evaluated. Surprisingly, for these data sets the depth of the network architecture did not affect the accuracy of the trained model. We also find that concerns about the computer processing time needed to train neural network models to perform well in genomic prediction tasks may not apply when Graphics Processing Units are used for model training.
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