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Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids

Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. Th...

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Autores principales: Galli, Giovanni, Sabadin, Felipe, Yassue, Rafael Massahiro, Galves, Cassia, Carvalho, Humberto Fanelli, Crossa, Jose, Montesinos-López, Osval Antonio, Fritsche-Neto, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936805/
https://www.ncbi.nlm.nih.gov/pubmed/35321444
http://dx.doi.org/10.3389/fpls.2022.845524
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author Galli, Giovanni
Sabadin, Felipe
Yassue, Rafael Massahiro
Galves, Cassia
Carvalho, Humberto Fanelli
Crossa, Jose
Montesinos-López, Osval Antonio
Fritsche-Neto, Roberto
author_facet Galli, Giovanni
Sabadin, Felipe
Yassue, Rafael Massahiro
Galves, Cassia
Carvalho, Humberto Fanelli
Crossa, Jose
Montesinos-López, Osval Antonio
Fritsche-Neto, Roberto
author_sort Galli, Giovanni
collection PubMed
description Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images.” In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.
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spelling pubmed-89368052022-03-22 Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids Galli, Giovanni Sabadin, Felipe Yassue, Rafael Massahiro Galves, Cassia Carvalho, Humberto Fanelli Crossa, Jose Montesinos-López, Osval Antonio Fritsche-Neto, Roberto Front Plant Sci Plant Science Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images.” In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding. Frontiers Media S.A. 2022-03-07 /pmc/articles/PMC8936805/ /pubmed/35321444 http://dx.doi.org/10.3389/fpls.2022.845524 Text en Copyright © 2022 Galli, Sabadin, Yassue, Galves, Carvalho, Crossa, Montesinos-López and Fritsche-Neto. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Galli, Giovanni
Sabadin, Felipe
Yassue, Rafael Massahiro
Galves, Cassia
Carvalho, Humberto Fanelli
Crossa, Jose
Montesinos-López, Osval Antonio
Fritsche-Neto, Roberto
Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
title Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
title_full Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
title_fullStr Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
title_full_unstemmed Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
title_short Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
title_sort automated machine learning: a case study of genomic “image-based” prediction in maize hybrids
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936805/
https://www.ncbi.nlm.nih.gov/pubmed/35321444
http://dx.doi.org/10.3389/fpls.2022.845524
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