A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome

Abstract N6-methyladenine (m6A) is one of the crucial epigenetic modifications and is related to the control of various DNA processes. Carrying out a genome-wide m6A analysis via wet experiments is fundamental but takes a long time. As complementary methods, computing tools, especially those based on machine learning, are urgently needed. A new protocol, iRicem6A-CNN, for identifying m6A sites in the rice genome was developed. This protocol was designed to use dinucleotide one-hot encoding to generate input tensors for predictions by convolutional neutral networks, and achieved five-fold cross-validation and independent testing accuracy values of 93.82% and 96.19%, respectively, performing better than those of other available predictors. The experiment results demonstrates that only the ability of iRicem6A-CNN based on 2-mer one-hot encoding is to display high performance but also to perform more stably and robustly than models using 1-mer one-hot encoding. A webserver is accessible at http://iRicem6A-CNN.aibiochem.net