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Deep learning genomic-enabled prediction of plant traits

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Deep learning genomic-enabled prediction of plant traits
 
Identifier https://hdl.handle.net/11529/10548082
 
Creator Montesinos-López, Abelardo
Montesinos-López, Osval A.
Gianola, Daniel
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Machine learning (ML) is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) from data, without being explicitly programmed to do this. ML is closely related to (and often overlaps with) computational statistics, which also focuses on making predictions through the use of computers. In general, ML explores algorithms that can learn from current data and make predictions on new data, through building a model from sample inputs. The field of statistics and ML had a root in common and will continue to come closer together in the future. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. DL models with densely connected network architecture were compared with one of the most often used genome-enabled prediction models genomic best linear unbiased prediction (GBLUP). We used nine published real genomic data sets to compare the models and obtain a “meta picture” of the performance of DL models with a densely connected network architecture.
 
Subject Agricultural Sciences
Agricultural research
Deep learning
Genomic selection
Prediction accuracy
Neural network
Genomic best linear unbiased prediction
GBLUP
 
Language English
 
Date 2018-06-13
 
Contributor Shrestha, Rosemary
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
CGIAR
 
Type Experimental data