ResDeepGS: A deep learning-based method for crop phenotype prediction.

Area of Science:

• Agricultural Science
• Genetics
• Computational Biology

Background:

• Genomic selection (GS) uses genomic markers for crop and animal breeding.
• Traditional methods struggle with complex gene interactions and large datasets.
• Deep learning offers potential for capturing nonlinear relationships and gene interactions.

Purpose of the Study:

• To propose a novel crop phenotype prediction method, ResDeepGS, using deep learning.
• To improve the efficiency and reliability of feature selection in genomic data.
• To enhance the accuracy of predicting crop traits for accelerated breeding.

Main Methods:

• Developed ResDeepGS, a deep learning model with feature selection and phenotype prediction modules.
• Utilized incremental recursive feature elimination for efficient feature selection.
• Employed an enhanced multi-layer convolutional neural network with residual structures and dropout for phenotype prediction.

Main Results:

• ResDeepGS outperformed state-of-the-art methods on wheat, maize, and soybean datasets.
• Achieved 5-9% improvement in prediction accuracy on the wheat dataset.
• Demonstrated superior performance in genomic selection tasks.

Conclusions:

• ResDeepGS offers a robust and adaptable solution for genomic selection.
• The method enhances crop breeding efficiency and contributes to addressing food security.
• Deep learning advancements significantly improve phenotype prediction accuracy.