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Machine and deep learning approaches have been largely responsible for a recent paradigm shift in image and natural language processing, which are among the fundamental enablers of modern artificial intelligence advances such as facial recognition, speech recognition, and self-driving vehicles. These deep-learning methodologies have been applied to biology such as agriculture and genetics. Here, we first developed a deep learning models trained with previous released 6mA data for predicting and memorizing on both the motifs and the functional evolutionary history of 6mA in rice. With these deep learning of trained data, we tested 6mA in two Nip and 93-11 cultivars for predicting 6mA site from the whole rice genome region. The architectures will allow all rice researchers easily enquiry and predict the potential 6mA site on targeted genes or regions.
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