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# PathCNN: Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma |
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# PathCNN: Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma |
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Jung Hun Oh <sup>1,†,∗</sup>, Wookjin Choi <sup>2,†</sup>, Euiseong Ko <sup>3</sup>, Mingon Kang <sup>3,∗</sup>, Allen Tannenbaum <sup>4</sup> and Joseph O. Deasy <sup>1</sup> |
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Jung Hun Oh <sup>1,†,∗</sup>, Wookjin Choi <sup>2,†</sup>, Euiseong Ko <sup>3</sup>, Mingon Kang <sup>3,∗</sup>, Allen Tannenbaum <sup>4</sup> and Joseph O. Deasy <sup>1</sup> |
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<sup>1</sup>Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA, |
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<sup>1</sup>Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA,
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<sup>2</sup>Department of Computer Science, Virginia State University, Petersburg, USA, |
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<sup>2</sup>Department of Computer Science, Virginia State University, Petersburg, USA,
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<sup>3</sup>Department of Computer Science, University of Nevada, Las Vegas, USA and |
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<sup>3</sup>Department of Computer Science, University of Nevada, Las Vegas, USA and
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<sup>4</sup>Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, New York, USA |
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<sup>4</sup>Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, New York, USA |
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<sup>*</sup>To whom correspondence should be addressed. |
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<sup>*</sup>To whom correspondence should be addressed.
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<sup>†</sup>The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. |
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<sup>†</sup>The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. |
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Contact: <ohj@mskcc.org> or <mingon.kang@unlv.edu> |
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Contact: <ohj@mskcc.org> or <mingon.kang@unlv.edu> |
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1. Model Building |
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1. Model Building
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- PathCNN.py |
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- PathCNN.py |
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2. GradCAM |
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2. GradCAM
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- PathCNN_GradCAM_modeling.py: to generate a model for GradCAM (PathCNN_model.h5) |
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- PathCNN_GradCAM_modeling.py: to generate a model for GradCAM (PathCNN_model.h5)
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- PathCNN_GradCAM.py: to generate GradCAM images and a resultant file (pathcnn_gradcam.csv) |
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- PathCNN_GradCAM.py: to generate GradCAM images and a resultant file (pathcnn_gradcam.csv) |
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3. Multi-omics data |
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3. Multi-omics data
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- GBM multi-omics data including mRNA expression, CNV, and DNA methylation were downloaded from the CBioPortal database. |
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- GBM multi-omics data including mRNA expression, CNV, and DNA methylation were downloaded from the CBioPortal database.
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- Pathway information was downloaded from the KEGG database. |
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- Pathway information was downloaded from the KEGG database.
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- PCA was performed for each pathway in individual omics types. |
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- PCA was performed for each pathway in individual omics types.
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Five PCs in each omics type are in the following files: |
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Five PCs in each omics type are in the following files:
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- PCA_EXP.xlsx, PCA_CNV.xlsx, PCA_MT.xlsx |
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- PCA_EXP.xlsx, PCA_CNV.xlsx, PCA_MT.xlsx
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Clinival variables are in the following file: |
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Clinival variables are in the following file:
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- Clinical.xlsx |
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- Clinical.xlsx
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Datasets
Models
