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Positive and negative datasets were obtained (Step 1). Features of the datasets were extracted to obtain 366-dimensional vectors for each site as training data. The SVM classifier was trained to generate the SVM model and the performance of the model was evaluated (Step 2). Human transcriptome-wide m6A sites were predicted and a web server was constructed (Step 3).
