ArticleFrontiers in plant science2025
PRGminer: harnessing deep learning for the prediction of resistance genes involved in plant defense mechanisms.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Article
- WheatRPred: deep learning framework for predicting disease resistance protein in wheat and its wild relatives.Journal, genetic engineering & biotechnology · 2026Article
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Abstract
Plant resistance genes are crucial in plant defense systems against a variety of diseases and pests. These plant-specific genes encode proteins that identify particular molecular patterns associated with pathogens invading the plants. When these resistance genes are active, they initiate a sequence of molecular processes that culminate in the activation of defensive responses such as the synthesis of antimicrobial chemicals, cell wall strengthening, and triggering of programmed cell death in infected cells. Plant resistance genes are exceedingly varied, with several classes and subclasses found across a wide range of plant species. The identification of new resistance genes (Rgenes) is a critical component of disease resistance breeding. Nonetheless, identifying Rgenes in wild species and near relatives of plants is not only challenging but also time-consuming. In this study, we present PRGminer, a deep learning-based high-throughput Rgenes prediction tool. PRGminer is implemented in two phases: Phase I predicts the input protein sequences as Rgenes or non-Rgenes; and Phase II classify the Rgenes predicted in Phase I into eight different classes. Among all the sequence representations tested, the dipeptide composition gave the best prediction performance (accuracy of 98.75% in a
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