Genomic Selection (GS) predicts traits using genome-wide markers, speeding up genetic progress and enhancing breeding efficiency. Recent emphasis has been placed on deep learning models to enhance prediction accuracy. However, current deep learning models focus on learning specific phenotypes for the given task, overlooking the inter-correlations among different phenotypes. In response, we introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space. Extensive experiments reveal that MtCro outperforms mainstream models, including DNNGP and SoyDNGP, with performance gains of 1-9% on the Wheat2000 dataset, 1-8% on Wheat599, and 1-3% on Maize8652. Furthermore, comparative analysis shows a consistent 2-3% improvement in multi-phenotype predictions, emphasizing the impact of inter-phenotype correlations on accuracy. By leveraging multi-task learning, MtCro efficiently captures diverse plant phenotypes, enhancing both model training efficiency and prediction accuracy, ultimately accelerating the progress of plant genetic breeding. Our code is available on https://github.com/chaodian12/mtcro .

The role of rice genomics in breeding progress is becoming increasingly important. Deeper research into the rice genome will contribute to the identification and utilization of outstanding functional genes, enriching the diversity and genetic basis of breeding materials and meeting the diverse demands for various improvements. Here, we review the significant contributions of rice genomics research to breeding progress over the last 25 years, discussing the profound impact of genomics on rice genome sequencing, functional gene exploration, and novel breeding methods, and we provide valuable insights for future research and breeding practices.

Genomic selection serves as an effective way for crop genetic breeding, capable of significantly shortening the breeding cycle and improving the accuracy of breeding. Phenotype prediction can help identify genetic variants associated with specific phenotypes. This provides a data-driven selection criterion for genomic selection, making the selection process more efficient and targeted. Deep learning has become an important tool for phenotype prediction due to its abilities in automatic feature learning, nonlinear modeling, and high-dimensional data processing. Current deep learning models have improvements in various aspects, such as predictive performance and computation time, but they still have limitations in capturing the complex relationships between genotype and phenotype, indicating that there is still room for improvement in the accuracy of phenotype prediction. This study innovatively proposes a new method called DeepAT, which mainly includes an input layer, a data feature extraction layer, a feature relationship capture layer, and an output layer. This method can predict wheat yield based on genotype data and has innovations in the following four aspects: (1) The data feature extraction layer of DeepAT can extract representative feature vectors from high-dimensional SNP data. By introducing the ReLU activation function, it enhances the model’s ability to express nonlinear features and accelerates the model’s convergence speed; (2) DeepAT can handle high-dimensional and complex genotype data while retaining as much useful information as possible; (3) The feature relationship capture layer of DeepAT effectively captures the complex relationships between features from low-dimensional features through a self-attention mechanism; (4) Compared to traditional RNN structures, the model training process is more efficient and stable. Using a public wheat dataset from AGT, comparative experiments with three machine learning and six deep learning methods found that DeepAT exhibited better predictive performance than other methods, achieving a prediction accuracy of 99.98%, a mean squared error (MSE) of only 28.93 tones, and a Pearson correlation coefficient close to 1, with yield predicted values closely matching observed values. This method provides a new perspective for deep learning-assisted phenotype prediction and has great potential in smart breeding.

As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.

Single-cell RNA sequencing (scRNA-seq) technology enables a deep understanding of cellular differentiation during plant development and reveals heterogeneity among the cells of a given tissue. However, the computational characterization of such cellular heterogeneity is complicated by the high dimensionality, sparsity, and biological noise inherent to the raw data. Here, we introduce PhytoCluster, an unsupervised deep learning algorithm, to cluster scRNA-seq data by extracting latent features. We benchmarked PhytoCluster against four simulated datasets and five real scRNA-seq datasets with varying protocols and data quality levels. A comprehensive evaluation indicated that PhytoCluster outperforms other methods in clustering accuracy, noise removal, and signal retention. Additionally, we evaluated the performance of the latent features extracted by PhytoCluster across four machine learning models. The computational results highlight the ability of PhytoCluster to extract meaningful information from plant scRNA-seq data, with machine learning models achieving accuracy comparable to that of raw features. We believe that PhytoCluster will be a valuable tool for disentangling complex cellular heterogeneity based on scRNA-seq data.