Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784672251986051072 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8936805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT galligiovanni automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT sabadinfelipe automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT yassuerafaelmassahiro automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT galvescassia automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT carvalhohumbertofanelli automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT crossajose automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT montesinoslopezosvalantonio automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids AT fritschenetoroberto automatedmachinelearningacasestudyofgenomicimagebasedpredictioninmaizehybrids |