What question did this study set out to answer?

The study aims to develop precise methods for identifying disease-resistance genes in plants to enhance crop breeding.

Machine learning empowers precise discovery of disease-resistance genes in plants

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Randomized trial reveals an innovative model for annotating disease-resistance genes in plants, suggesting advancements in crop breeding.

The study aims to develop precise methods for identifying disease-resistance genes in plants to enhance crop breeding.
Proposed ESM-LRR, a deep protein language model for predicting LRR domains in proteins.
Developed R-Predictor to annotate diverse domain topologies across the plant genome.
Integrated gene expression profiles with R-Predictor to identify candidate resistance genes.
ESM-LRR achieved a maximum F1 score of 0.80 using 90% identity as the threshold.
R-Predictor outperformed existing methods with F1 scores of 0.89 for RLKs and 0.88 for NLRs.
R-Predictor detected dozens of candidate R genes linked to grape gray mold and downy mildew.

Liu et al. (2026) studied this question.

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