This repository is the implementation of "Graph Neural Network for Protein-Protein Interaction Prediction: A Comparative Study". It is based on the hgcn repository with additional PPI data and some code optimization. All models including NN, GCN, GAT, HNN, HGCN are implementated with Pytorch.
If you don't have conda installed, please install it following the instructions here.
git clone https://github.com/ZJUDataIntelligence/GNN-PPI.git
cd GNN-PPI
conda env create -f environment.yml
Before training, run
source set_env.sh
This will create environment variables that are used in the code.
In order to run the GNN model on the PPI dataset，please set the --task option as "lp" and set --dataset option as "ppi", choose GNN model with --model option, choose dataset with --ppitype and --ppimode options. The complete command can be referred to the following example.
optional arguments:
-h, --help show this help message and exit
--lr LR learning rate
--dropout DROPOUT dropout probability
--cuda CUDA which cuda device to use (-1 for cpu training)
--epochs EPOCHS maximum number of epochs to train for
--weight-decay WEIGHT_DECAY
l2 regularization strength
--optimizer OPTIMIZER
which optimizer to use, can be any of [Adam,
RiemannianAdam]
--momentum MOMENTUM momentum in optimizer
--patience PATIENCE patience for early stopping
--seed SEED seed for training
--log-freq LOG_FREQ how often to compute print train/val metrics (in
epochs)
--eval-freq EVAL_FREQ
how often to compute val metrics (in epochs)
--save SAVE 1 to save model and logs and 0 otherwise
--save-dir SAVE_DIR path to save training logs and model weights (defaults
to logs/task/date/run/)
--sweep-c SWEEP_C
--lr-reduce-freq LR_REDUCE_FREQ
reduce lr every lr-reduce-freq or None to keep lr
constant
--gamma GAMMA gamma for lr scheduler
--print-epoch PRINT_EPOCH
--grad-clip GRAD_CLIP
max norm for gradient clipping, or None for no
gradient clipping
--min-epochs MIN_EPOCHS
do not early stop before min-epochs
--task TASK which tasks to train on, can be any of [lp, nc]
--model MODEL which encoder to use, can be any of [Shallow, MLP,
HNN, GCN, GAT, HGCN]
--dim DIM embedding dimension
--manifold MANIFOLD which manifold to use, can be any of [Euclidean,
Hyperboloid, PoincareBall]
--c C hyperbolic radius, set to None for trainable curvature
--r R fermi-dirac decoder parameter for lp
--t T fermi-dirac decoder parameter for lp
--pretrained-embeddings PRETRAINED_EMBEDDINGS
path to pretrained embeddings (.npy file) for Shallow
node classification
--pos-weight POS_WEIGHT
whether to upweight positive class in node
classification tasks
--num-layers NUM_LAYERS
number of hidden layers in encoder
--bias BIAS whether to use bias (1) or not (0)
--act ACT which activation function to use (or None for no
activation)
--n-heads N_HEADS number of attention heads for graph attention
networks, must be a divisor dim
--alpha ALPHA alpha for leakyrelu in graph attention networks
--use-att USE_ATT whether to use hyperbolic attention in HGCN model
--double-precision DOUBLE_PRECISION
whether to use double precision
--dataset DATASET which dataset to use
--val-prop VAL_PROP proportion of validation edges for link prediction
--test-prop TEST_PROP
proportion of test edges for link prediction
--use-feats USE_FEATS
whether to use node features or not
--normalize-feats NORMALIZE_FEATS
whether to normalize input node features
--normalize-adj NORMALIZE_ADJ
whether to row-normalize the adjacency matrix
--split-seed SPLIT_SEED
seed for data splits (train/test/val)
--ppitype DATASET
using SHS27 or SHS248 dataset
--ppimode DATASET
ppi mode, including activation, binding, catalysis, expression, inhibition, ptmod or reaction
python train.py --task lp --dataset ppi --model HGCN --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.4 --weight-decay 0.0001 --manifold PoincareBall --cuda 0 --log-freq 5 --ppitype SHS27 --ppimode activation