文献类型： 外文期刊

作者： Montesinos-Lopez, Osval A. ¹ ; Chavira-Flores, Moises ² ; Kiasmiantini, Leo ¹ ; Crespo-Herrera, Leo ³ ; Saint Piere, Carolina ³ ; Li, Huihui ⁴ ; Fritsche-Neto, Roberto ⁵ ; Al-Nowibet, Khalid ⁶ ; Montesinos-Lopez, Abelardo ⁷ ; Crossa, Jose ³ ;

作者机构： 1.Univ Colima, Fac Telematica, Colima 28040, Colima, Mexico

2.Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas IIMAS, Mexico City 04510, Mexico; Univ Negeri Yogyakarta, Stat Study Program, Yogyakarta 55281, Indonesia

3.Int Maize & Wheat Improvement Ctr CIMMYT, Km 45,Carretera Mexico Veracruz, Texcoco 52640, Edo De Mexico, Mexico

4.Chinese Acad Agr Sci CAAS, Inst Crop Sci, Chin Off, 12 Zhongguancun,South St, Beijing 100081, Peoples R China

5.Louisiana State Univ, Baton Rouge, LA 70803 USA

6.King Saud Univ, Dept Stat & Operat Res, Riyadh 11451, Saudi Arabia

7.Univ Guadalajara, Ctr Univ Ciencias Exactas & Ingn CUCEI, Dept Matemat, Guadalajara 44430, Jalisco, Mexico

8.Colegio Postgrad, Montecillos 56230, Edo De Mexico, Mexico

9.King Saud Univ, Distinguish Scientist Fellowship Program, Riyah 11451, Saudi Arabia

关键词： data fusion; genomic prediction; multimodal deep learning; plant breeding

期刊名称：GENETICS （ 影响因子：5.1； 五年影响因子：4.1 ）

ISSN： 0016-6731

年卷期： 2024 年 228 卷 4 期

页码：

收录情况： SCI

摘要： 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.