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The detailed model architecture of PromBERT for promoter prediction. DNA sequences of fixed length are tokenized using a custom 1bp tokenizer and fed into 144 attention modules. Based on the final tensors in the pooling layer, the classifier calculates the probabilities of promoter and non-promoter using the sigmoid function. Backpropagation was conducted using binary cross entropy loss.
