This is the code that is associated with the paper title above. Credit most go to Michaël Defferrard en al for using his code as a base for the adaptation of this code, the citation is shown below. the code was modified to follow the GCN model decribed in Kipf's "Semi-Supervised Classification with Graph Convolutional Networks".

1. Clone this repository.

   git clone https://github.com/RicardoRamirez2020/GCN_Cancer
   cd GCN_Cancer

2. Install the dependencies. The code should run with TensorFlow 1.0 and newer.

   pip install -r requirements.txt  # or make install

cd GCN_Cancer
PPI.py

To use the model you need

1. A data matrix where each row is a sample and each column is a feature
2. Target Labels
3. An adjacency matrix which encodes the structure as a graph.

To use the GCN model given by Kipf, keep the K hyper parameter to 1 and implement the renormalization to your graph. Data can be sent individually.

Data can be downloaded at, https://drive.google.com/drive/folders/1_Cnvab7mIwCrNJyY-J4aR2ck9i72KH8t?usp=sharing

Please perfer to https://github.com/mdeff/cnn_graph for installation and other value sources about the topic.