realization DeepSurv and Coxnnet in TCGA mi-RNA analysis
This project's paper includes a section dedicated to its deep learning analysis, specifically focusing on the implementation of the XGBENC deep learning analysis: XGBENC
This project uses deep learning methods to implement prognostic analysis of TCGA-miRNA. We propose a scalable code framework covering grid search, 5-fold cross-validation methods. This project analyzes two veteran survival analysis algorithms. DeepSurv and Coxnnet and give model evaluation.
Pytorch>=0.4.0
CPU or GPU
<pip install requirements.txt>
you should download from TCGA and convert to s-g_data100_data.csv format. TCGA
you can start in you CMD via
<python main.py>
network.py contains all the Network settings and Partial Likelihood loss function
ini_file.py contain all the hyper parameters
utils.py contain the c-index calculation and other settings
you can run DL_survival_main to run the model.
czifan/DeepSurv.pytorch (czifan@pku.edu.cn)
if you have any problems, please contact Wankang Zhai (wzhai2@uh.edu)