Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and...

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Autores principales:	Zhanyou Xu, Andreomar Kurek, Steven B Cannon, William D Beavis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d09932bcc7aa4dac9b44723fcca3a9b8
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spelling	oai:doaj.org-article:d09932bcc7aa4dac9b44723fcca3a9b82021-12-02T20:05:09ZPredictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.1932-620310.1371/journal.pone.0240948https://doaj.org/article/d09932bcc7aa4dac9b44723fcca3a9b82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0240948https://doaj.org/toc/1932-6203In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the 'wrong' genotypes retained, and how often are the 'correct' genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.Zhanyou XuAndreomar KurekSteven B CannonWilliam D BeavisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0240948 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhanyou Xu Andreomar Kurek Steven B Cannon William D Beavis Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
description	In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the 'wrong' genotypes retained, and how often are the 'correct' genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.
format	article
author	Zhanyou Xu Andreomar Kurek Steven B Cannon William D Beavis
author_facet	Zhanyou Xu Andreomar Kurek Steven B Cannon William D Beavis
author_sort	Zhanyou Xu
title	Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
title_short	Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
title_full	Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
title_fullStr	Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
title_full_unstemmed	Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
title_sort	predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d09932bcc7aa4dac9b44723fcca3a9b8
work_keys_str_mv	AT zhanyouxu predictionsfromalgorithmicmodelingresultinbetterdecisionsthanfromdatamodelingforsoybeanirondeficiencychlorosis AT andreomarkurek predictionsfromalgorithmicmodelingresultinbetterdecisionsthanfromdatamodelingforsoybeanirondeficiencychlorosis AT stevenbcannon predictionsfromalgorithmicmodelingresultinbetterdecisionsthanfromdatamodelingforsoybeanirondeficiencychlorosis AT williamdbeavis predictionsfromalgorithmicmodelingresultinbetterdecisionsthanfromdatamodelingforsoybeanirondeficiencychlorosis
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Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.

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