A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional me...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2024
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| Acceso en línea: | https://hdl.handle.net/10568/163651 |
| _version_ | 1855524352938737664 |
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| author | Montesinos-Lopez, Osval A. Chavira-Flores, Moises Kismiantini Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Huihui Li Fritsche-Neto, Roberto Khalid Al-Nowibet Montesinos-Lopez, Abelardo Crossa, José |
| author_browse | Chavira-Flores, Moises Crespo-Herrera, Leonardo A. Crossa, José Fritsche-Neto, Roberto Huihui Li Khalid Al-Nowibet Kismiantini Montesinos-Lopez, Abelardo Montesinos-Lopez, Osval A. Saint Pierre, Carolina |
| author_facet | Montesinos-Lopez, Osval A. Chavira-Flores, Moises Kismiantini Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Huihui Li Fritsche-Neto, Roberto Khalid Al-Nowibet Montesinos-Lopez, Abelardo Crossa, José |
| author_sort | Montesinos-Lopez, Osval A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population. |
| format | Journal Article |
| id | CGSpace163651 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1636512025-05-04T09:21:43Z A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding Montesinos-Lopez, Osval A. Chavira-Flores, Moises Kismiantini Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Huihui Li Fritsche-Neto, Roberto Khalid Al-Nowibet Montesinos-Lopez, Abelardo Crossa, José data fusion genomics learning plant breeding forecasting Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population. 2024-12 2024-12-17T16:14:20Z 2024-12-17T16:14:20Z Journal Article https://hdl.handle.net/10568/163651 en Open Access application/pdf Oxford University Press Montesinos-López, O. A., Chavira-Flores, M., Kiasmiantini, Crespo-Herrera, L., Saint Piere, C., Li, H., Fritsche-Neto, R., Al-Nowibet, K., Montesinos-López, A., & Crossa, J. (2024). A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding. GENETICS, 228(4), iyae161. https://doi.org/10.1093/genetics/iyae161 |
| spellingShingle | data fusion genomics learning plant breeding forecasting Montesinos-Lopez, Osval A. Chavira-Flores, Moises Kismiantini Crespo-Herrera, Leonardo A. Saint Pierre, Carolina Huihui Li Fritsche-Neto, Roberto Khalid Al-Nowibet Montesinos-Lopez, Abelardo Crossa, José A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding |
| title | A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding |
| title_full | A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding |
| title_fullStr | A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding |
| title_full_unstemmed | A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding |
| title_short | A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding |
| title_sort | review of multimodal deep learning methods for genomic enabled prediction in plant breeding |
| topic | data fusion genomics learning plant breeding forecasting |
| url | https://hdl.handle.net/10568/163651 |
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