k-Nearest neighbour machine method for predicting resistance gene against Magnaporthe oryzae in rice using proteomic markers

Rice blast disease, caused by the fungal pathogen Magnaporthe oryzae, poses a severe threat to global rice cultivation, impacting over 3.5 billion people and the livelihoods of 200 million. Despite the challenges in achieving sustainable resistance, our study focuses on identifying the proteomic signatures in blast disease-resistant and susceptible genes using amino acid and dipeptide compositions. By leveraging machine learning, particularly a k-Nearest Neighbour model, we identified 20 molecular markers distinguishing between resistant and susceptible genes with 90% accuracy. This research highlights the potential of protein sequence-based machine learning for predicting blast disease resistance, providing valuable insights for disease-resistant breeding programs and enhancing global food security through sustainable rice cultivation.