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.
This repository is based on Tensorflow 2.2.0 For installing tensorflow, please follow the official instructions in here. The code is tested under Python 3.6 on Ubuntu 18.04.
Associate packages include: h5py, SHAP, sklearn.
Our prepared data can be downloaded from CCL-Discovery(data). Put all files in this folder to data_process
folder in the root directory.
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Setup the parameters accordingly in
option.py
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Train the model for our autoencoder to obtain SHAP scores. Run:
cd code python mlc-ae.py --phase train
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Test the model of autoencoder and draw the SHAP visualization. Run:
cd code python mlc-ae.py --phase test
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Train the model for our evaluation classifier, in where we have attached sample score files. Run:
cd code python eval-classifier.py --phase train
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Test the model for our evaluation classifier. Run:
cd code python eval-classifier.py --phase test