Figure S1 from A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes

The DEEP Neural Network Architecture and workflow of the DEEP score calculation for simple somatic mutations in prostate cancer. Layer1: convolution layer, kernels:320, window size:8, step size:1 Layer2: convolution layer, kernels:320, window size:8, step size:1 Layer3: pooling layer, kernel size: 4; step size:4; Layer4: convolution layer, kernels:480, window size:8, step size:1 Layer5: convolution layer, kernels:480, window size:8, step size:1 Layer6: pooling layer, kernel size: 4; step size:4; Layer7: convolution layer, kernels:640, window size:8, step size:1 Layer8: convolution layer, kernels:640, window size:8, step size:1 Layer9,10: fully connected layers; Layer11: Sigmoid output layer; All convolution layers were ReLU activated. 20% dropout were performed before the pooling layers. The weight (W) is a function that decays with the increased distance between mutations and the 200bp windows. We used k=5 as a magnifier. Finally, absolute values of the summed scores from two strands were taken to derive the DEEP scores.