PathCNN

Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

  • Pathway image: Grid structure conversion for biological array data (a non-grid structured format) for CNNs.
  • Interpretation of the CNN model using GradCAM.

Source code: https://github.com/mskspi/PathCNN

Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O Deasy, PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma, Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i443–i450, https://doi.org/10.1093/bioinformatics/btab285

PathCNN
  1. Model Building
    • PathCNN.py
  2. GradCAM
    • PathCNN_GradCAM_modeling.py: to generate a model for GradCAM (PathCNN_model.h5)
    • PathCNN_GradCAM.py: to generate GradCAM images and a resultant file (pathcnn_gradcam.csv)
  3. Multi-omics data
    • GBM multi-omics data including mRNA expression, CNV, and DNA methylation were downloaded from the CBioPortal database.
    • Pathway information was downloaded from the KEGG database.
    • PCA was performed for each pathway in individual omics types.
    Five PCs in each omics type are in the following files:
    • PCA_EXP.xlsx, PCA_CNV.xlsx, PCA_MT.xlsx
    Clinival variables are in the following file:
    • Clinical.xlsx

Published by Wookjin Choi

Assistant Professor Department of Radiation Oncology Thomas Jefferson University

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