Prerequisites Installation
Python 3.7 - https://www.python.org/downloads/release/python-370/
Numpy 1.21.5 - https://pypi.org/project/numpy/1.21.5/
Pandas 1.3.5 - https://pypi.org/project/pandas/1.3.5/
Scipy 1.7.3 - https://pypi.org/project/scipy/1.7.3/
Scikit-learn 1.0.2 - https://pypi.org/project/scikit-learn/1.0.2/
Theano 1.0.3 - https://pypi.org/project/Theano/1.0.3/
Tensorflow 1.13.1 - https://pypi.org/project/tensorflow/1.13.1/
Tensorflow-estimator 1.13.0 - https://pypi.org/project/tensorflow-estimator/1.13.0/
Tensorboard 1.13.1 - https://pypi.org/project/tensorboard/1.13.1/
Keras 2.2.4 - https://pypi.org/project/keras/2.2.4/
Keras-applications 1.0.8 - https://pypi.org/project/Keras-Applications/
Keras-preprocessing 1.1.0 - https://pypi.org/project/Keras-Preprocessing/1.1.0/
Pytorch 1.10.2 - https://pypi.org/project/torch/1.10.2/
Lasagne 0.2.dev1 - https://github.com/Lasagne/Lasagne
Xlrd 1.1.0 - https://pypi.org/project/xlrd/1.1.0/
openpyxl - https://pypi.org/project/openpyxl/
Running the codes
STEP 1 - Download the required dataset from the link provided in the respective subfolder in the path './Dataset/EssentialData/'
STEP 2 - In line 27 of main.py file, enter the feature_type as an input argument
STEP 3 - Run main.py with input arguments using the following command:
python main.py <cancer_type> <clinical_outcome_endpoint> <event_time_threshold> <target_minority_group> <features_count>
After execution, the result will be saved in the './Result/' folder as an excel file.
Acknowledgement
This work has been supported by NIH R01 grant.
Contact
For any queries, please contact:
Prof. Yan Cui (ycui2@uthsc.edu)
Dr. Teena Sharma (tee.shar6@gmail.com)