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

Domaine:

healthcare

Type de record:

paper
Créateur:
CheJin
Éditeur:
Ame
Hôte:

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.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

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