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+# Discovery of Primary Prostate Cancer Biomarkers using Cross-Cancer Learning
+
+### Introduction
+
+This repository is for our submitted paper for Scientific Reports '[Discovery of Primary Prostate Cancer Biomarkers
+using Cross-Cancer Learning]'. The code is modified from [DeePathology](https://github.com/SharifBioinf/DeePathology). 
+
+### Installation
+This repository is based on Tensorflow 2.2.0
+For installing tensorflow, please follow the official instructions in [here](https://www.tensorflow.org/install/install_linux). The code is tested under Python 3.6 on Ubuntu 18.04.
+
+Associate packages include: [h5py](https://www.h5py.org/), [SHAP](https://github.com/slundberg/shap), [sklearn](https://scikit-learn.org/stable/).
+
+### Data
+Our prepared data can be downloaded from [CCL-Discovery(data)](https://drive.google.com/file/d/1evJ7J4M7U8TsU_lujKUBq6ROYS9d7pZR/view?usp=sharing). Put all files in this folder to `data_process` folder in the root directory.
+### Usage
+
+1. Setup the parameters accordingly in `option.py`
+   
+2. Train the model for our autoencoder to obtain SHAP scores.
+    Run:
+   ```shell
+   cd code
+   python mlc-ae.py --phase train
+   ```
+
+3. Test the model of autoencoder and draw the SHAP visualization.
+    Run:
+   ```shell
+   cd code
+   python mlc-ae.py --phase test
+   ```  
+   
+4. Train the model for our evaluation classifier, in where we have attached sample score files.
+    Run:
+   ```shell
+   cd code
+   python eval-classifier.py --phase train
+   ```
+   
+5. Test the model for our evaluation classifier.
+    Run:
+   ```shell
+   cd code
+   python eval-classifier.py --phase test
+   ```