Prediction of Clinical Features Using Graph Neural Networks

Discovery of functional motifs by association to clinical features using Graph Neural Networks.

Running

Training PNA For Regression

python train_test_controller.py --aggregators 'max' --bs 16 --dropout 0.0 --en my_experiment --epoch 200 --factor 0.8 --fcl 256 --gcn_h 64 --lr 0.001 --min_lr 0.0001 --model PNAConv --num_of_ff_layers 1 --num_of_gcn_layers 2 --patience 5 --scalers 'identity' --weight_decay 1e-05

Training GAT For Regression

python train_test_controller.py --aggregators None --bs 16 --dropout 0.0 --en my_experiment --epoch 200 --factor 0.2 --fcl 128 --gcn_h 64 --lr 0.001 --min_lr 2e-05 --model GATConv --num_of_ff_layers 1 --num_of_gcn_layers 3 --patience 20 --scalers None --weight_decay 0

GNNExplainer For PNA Regressor

python gnnexplainer.py --aggregators 'max' --bs 16 --dropout 0.0 --fcl 256 --gcn_h 64 --model PNAConv --num_of_ff_layers 1 --num_of_gcn_layers 2 --scalers 'identity' --idx 10

LIME Explainer for PNA Regressor

python lime.py --aggregators 'max' --bs 16 --dropout 0.0 --fcl 256 --gcn_h 64 --model PNAConv --num_of_ff_layers 1 --num_of_gcn_layers 2 --scalers 'identity' --idx 10

SHAP Explainer for PNA Regressor

python shap.py --aggregators 'max' --bs 16 --dropout 0.0 --fcl 256 --gcn_h 64 --model PNAConv --num_of_ff_layers 1 --num_of_gcn_layers 2 --scalers 'identity' --idx 10

Explainable cells and cell interactions

Original Graph	SubGraph

First Results of Hyperparameter Tuning