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GANs for Precision Oncology
Python Jupyter Notebook
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geneGAN
treatGAN
README.md
requirements.txt

README.md

Adversarial Training for Precision Oncology

Overview

This repo showcases two generative adversarial net (GAN) approaches to precision oncology. In each folder the scripts and notebooks to train a model and to apply a trained model are provided.

Installation

To install the necessary libraries run:

pip install -r requirements.txt

geneGAN

With a dataset of co-occurring gene pairs, train a GAN to learn the pair distribution in order to generate and discriminate co-occurring gene pairs.

treatGAN

With two dataset consisting of a patients disease, demographics and genetic information and the corresponding treatments train a conditional GAN to produce suitable treatment suggestions based on the patient information.

References

This work is based on the medGAN approach introduced in the following paper:

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun  
Machine Learning for Healthcare (MLHC) 2017
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