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Protein-protein Interaction Prediction

Protein-protein interaction prediction aims to identify and catalog physical interactions between pairs or groups of proteins. It is beneficial to gaining insights into various biochemical processes.

Features

  • Supported models: PIPR, GNN-PPI and OntoProtein. This is a continuing effort and we are working on further growing the list.
  • Supported datasets: SHS27k, SHS148k and STRING.
  • Supported split: Random; DFS; BFS.
  • Supported evaluation: Micro F1.

Data Preparation

All the datasets can downloaded here, and should put under datasets/ppi/.

Model Preparation

To reproduce OntoProtein, download the model from HuggingFace and put it under ckpts/protein_ckpts/.

Training and Evaluation

You can run scripts using bash under scripts/dti/:

scripts/aidd/ppi
├── run.sh             # running models

Example:

bash scripts/aidd/ppi cuda:0 # switch to your own cuda device or cpu

You can also modify the scripts or directly use the following command:

python open_biomed/tasks/prot_task/ppi.py \
--device DEVICE \                   # gpu device id
--mode MODE \                       # training mode, train / test
--config_path CONFIG_PATH \         # configuration file, see configs/ppi/ for more details
--dataset DATASET \                 # dataset name, select from [SHS27k, SHS148k, STRING]
--dataset_path DATASET_PATH \       # path to the dataset
--split_strategy SPLIT_STRATEGY \   # split strategy, select from [random, bfs, dfs]
--init_checkpoint INIT_CHECKPOINT \ # checkpoint path used for efficient validation
--param_key PARAM_KEY \             # key of the checkpoint dict that contains model parameters
--output_path OUTPUT_PATH \         # save checkpoint path used for training
--num_workers NUM_WORKERS \         # number of workers when loading data
--epochs EPOCHS \                   # number of training epochs
--patience PATIENCE \               # number of tolerant epochs for early-stopping
--weight_decay WEIGHT_DECAY \       # weight decay, default is 1e-5
--lr LR \                           # learning rate, default is 1e-3
--batch_size BATCH_SIZE \           # batch size, default is 512
--logging_steps LOGGING_STEPS \     # steps for printing training information
--seed SEED                         # random seed