UISNet: An uncertainty-based interpretable deep semi-supervised network for breast cancer outcomes prediction UISNet is designed to interpret the feature importance of the cancer outcomes prediction model by an uncertainty-based integrated gradients algorithm.
Before using, please unzip captur.rar in the current folder. The datasets aboout the gene expression and pathway information are stored in my_dataset folder. The output of the patients' risks, CI values and the IG scores with the Monte Carlo dropout are given in the result_all folder.
Due to file size constraints, here we give an example data: brca_test.csv (expression data) and pathway_mask (pathway information) used for NISNet.py. Users can build data as the format of the example. The main program UISNet.py can be used for cancer outcomes prediction.
This method is still on progress, any questions can be sent to 854330388@qq.com