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

Jung Hun Oh ^1,†,∗, Wookjin Choi ^2,†, Euiseong Ko ³, Mingon Kang ^3,∗, Allen Tannenbaum ⁴ and Joseph O. Deasy ¹

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA,
²Department of Computer Science, Virginia State University, Petersburg, USA,
³Department of Computer Science, University of Nevada, Las Vegas, USA and
⁴Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, New York, USA

^*To whom correspondence should be addressed.
^†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

Contact: ohj@mskcc.org or mingon.kang@unlv.edu

1. Model Building

2. PathCNN.py

3. GradCAM

4. PathCNN_GradCAM_modeling.py: to generate a model for GradCAM (PathCNN_model.h5)

5. PathCNN_GradCAM.py: to generate GradCAM images and a resultant file (pathcnn_gradcam.csv)

6. Multi-omics data

7. GBM multi-omics data including mRNA expression, CNV, and DNA methylation were downloaded from the CBioPortal database.
8. Pathway information was downloaded from the KEGG database.
9. 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