A simple implementation of the paper "Disease Prediction via Graph Neural Networks".
python >=3.6
pytorch
numpy
sklearn
"filename.nodes.pkl"
# list of node: [node1(str), node2(str), node3(str), ...]
"filename.adj.pkl"
# adj list of nodes:
# {node1(str): [neighbor1(str), neighbor2(str), ...], node2: []...}
"filename.rare.label.pkl"
# rare flag, indicating whether a node is a rare disease (value=1)
# or contains a rare disease, NumPy array of shape (N * 1)
"filename.label.pkl"
# NumPy array of shape (N * D), N is node number
# and D is the number of diseases
"filename.map.pkl"
# mapping node to index, {node(str): node_id(int), ...}
"filename.train.pkl"
# list of nodes for training, [node_idx_1(int), node_idx_2(int), ....]
"filename.test.pkl"
# list of nodes for testing, [node_idx_1(int), node_idx_2(int), ....]python run_multi.py