Genotype-to-Phenotype Prediction in Rice with High-Dimensional Nonlinear Features
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912247425531904 |
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| author | Zhou, Zeyuan Chen, Siyuan Wu, Xinzhang Zhang, Jisen Dong, Yunxuan |
| author_facet | Zhou, Zeyuan Chen, Siyuan Wu, Xinzhang Zhang, Jisen Dong, Yunxuan |
| contents | Genotype-to-Phenotype prediction can promote advances in modern genomic research and crop improvement, guiding precision breeding and genomic selection. However, high-dimensional nonlinear features often hinder the accuracy of genotype-to-phenotype prediction by increasing computational complexity. The challenge also limits the predictive accuracy of traditional approaches. Therefore, effective solutions are needed to improve the accuracy of genotype-to-phenotype prediction. In our paper, we propose MLFformer. MLFformer is a Transformer-based architecture that incorporates the Fast Attention mechanism and a multilayer perceptron module to handle high-dimensional nonlinear features. In MLFformer, the Fast Attention mechanism is utilized to handle computational complexity and enhance processing efficiency. In addition, the MLP structure further captures high-dimensional nonlinear features. Through experiments, the results show that MLFformer reduces the average MAPE by 7.73% compared to the vanilla Transformer. In univariate and multivariate prediction scenarios, MLFformer achieves the best predictive performance among all compared models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genotype-to-Phenotype Prediction in Rice with High-Dimensional Nonlinear Features Zhou, Zeyuan Chen, Siyuan Wu, Xinzhang Zhang, Jisen Dong, Yunxuan Genomics Genotype-to-Phenotype prediction can promote advances in modern genomic research and crop improvement, guiding precision breeding and genomic selection. However, high-dimensional nonlinear features often hinder the accuracy of genotype-to-phenotype prediction by increasing computational complexity. The challenge also limits the predictive accuracy of traditional approaches. Therefore, effective solutions are needed to improve the accuracy of genotype-to-phenotype prediction. In our paper, we propose MLFformer. MLFformer is a Transformer-based architecture that incorporates the Fast Attention mechanism and a multilayer perceptron module to handle high-dimensional nonlinear features. In MLFformer, the Fast Attention mechanism is utilized to handle computational complexity and enhance processing efficiency. In addition, the MLP structure further captures high-dimensional nonlinear features. Through experiments, the results show that MLFformer reduces the average MAPE by 7.73% compared to the vanilla Transformer. In univariate and multivariate prediction scenarios, MLFformer achieves the best predictive performance among all compared models. |
| title | Genotype-to-Phenotype Prediction in Rice with High-Dimensional Nonlinear Features |
| topic | Genomics |
| url | https://arxiv.org/abs/2502.18758 |