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: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164102 |
| _version_ | 1855527147023630336 |
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| author | Galli, Giovanni Sabadin, Felipe Yassue, Rafael Massahiro Galves, Cassia Carvalho, Humberto Fanelli Crossa, José Montesinos-López, Osval Antonio Fritsche-Neto, Roberto |
| author_browse | Carvalho, Humberto Fanelli Crossa, José Fritsche-Neto, Roberto Galli, Giovanni Galves, Cassia Montesinos-López, Osval Antonio Sabadin, Felipe Yassue, Rafael Massahiro |
| author_facet | Galli, Giovanni Sabadin, Felipe Yassue, Rafael Massahiro Galves, Cassia Carvalho, Humberto Fanelli Crossa, José Montesinos-López, Osval Antonio Fritsche-Neto, Roberto |
| author_sort | Galli, Giovanni |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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 | Journal Article |
| id | CGSpace164102 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1641022025-12-08T10:29:22Z 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, José Montesinos-López, Osval Antonio Fritsche-Neto, Roberto 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. 2022-03-07 2024-12-19T12:53:28Z 2024-12-19T12:53:28Z Journal Article https://hdl.handle.net/10568/164102 en Open Access Frontiers Media Galli, Giovanni; Sabadin, Felipe; Yassue, Rafael Massahiro; Galves, Cassia; Carvalho, Humberto Fanelli; Crossa, Jose; Montesinos-López, Osval Antonio and Fritsche-Neto, Roberto. 2022. "Automated machine learning: A case study of genomic ""image-based"" prediction in maize hybrids". Front. Plant Sci., Volume 13 |
| spellingShingle | plant science Galli, Giovanni Sabadin, Felipe Yassue, Rafael Massahiro Galves, Cassia Carvalho, Humberto Fanelli Crossa, José 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://hdl.handle.net/10568/164102 |
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