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Fig. 1 | BMC Genomics

Fig. 1

From: MRCNN: a deep learning model for regression of genome-wide DNA methylation

Fig. 1

The deep-learning architecture of MRCNN. The input layer is a matrix of one-hot coding for the DNA fragment centered at the methylation site, and the first convolution layer helps extract the information of each base. Then, it is reshaped as a 2D tensor for the following operations, and the convolution and pooling operations obtain higher-level sequence feature, while the next two convolution layers overcome the side effects of the saturated zone. Finally, the tensor is expanded by the full-connection layer, and the output node gives the prediction value

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