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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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GANs for Precision Oncology

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