https://github.com/mayurdivate/deepcancersignatures

This repository contains code used to build and interpret a deep learning model. It is a DNN classifier trained using gene expression data (TCGA). Then is interpreted to identify cancer specific gene expression signatures.
https://github.com/mayurdivate/deepcancersignatures

gene-signatures ipynb shap

Last synced: 8 months ago
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This repository contains code used to build and interpret a deep learning model. It is a DNN classifier trained using gene expression data (TCGA). Then is interpreted to identify cancer specific gene expression signatures.

• Host: GitHub
• URL: https://github.com/mayurdivate/deepcancersignatures
• Owner: MayurDivate
• Created: 2021-06-09T01:24:44.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2021-06-15T00:49:28.000Z (about 5 years ago)
• Last Synced: 2025-02-01T10:51:09.400Z (over 1 year ago)
• Topics: gene-signatures, ipynb, shap
• Language: Jupyter Notebook
• Homepage:
• Size: 688 KB
• Stars: 9
• Watchers: 2
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# DeepCancerSignatures

This repository contains code used to build and interpret a deep learning model.

It is a DNN classifier trained using gene expression data (TCGA).

Then is interpreted to identify cancer specific gene expression signatures.

![DNN Network](./images/network.png)  

repository contains following jupyter notebooksi and should be used in order below, 

1. Model.ipynb

- train test deep learning models 

2. ShapInterpretation.ipynb

- shap value calculation for each feature 

3. MergeShapChunks.ipynb 

- merge shap files to create one file per model

4. ShapTopGenes.ipynb 

- Get top 20 genes for each cancer types 

5. SelectGeneSignaturesFC.ipynb

- Filtering of gene to produce final list of gene signatures