Skip to content

emmatysinger/GearNet-ProtGNN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

6705930 · Sep 26, 2024

History

2 Commits
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024
Sep 26, 2024

Repository files navigation

GearNet-ProtGNN: Protein function prediction model from protein structure and biomedical knowledge

This repository hosts the official implementation of GearNet-ProtGNN, a model for predicting protein function.

Path

  • Must change line 13 in os.environ['WAND_EXECUTABLE'] to local path to python in training_downstream.py

Training

training_gearnetprotgnn.py is the main script to train GearNet-ProtGNN. Parameters to change:

  • num_epoch: Number of epochs to train model for
  • hyperparameter: True or False (run a hyperparameter sweep or not)
  • embed_file: path to pickle file of the protein embeddings from ProtGNN The script can be run from the command line or in a bash script like this:
python training_gearnetprotgnn.py

Downstream Prediction Tasks

training_downstream.py is the main script to run downstream prediction tasks. Parameters to change:

  • model_path: Change to path where your model is stored
  • dataset_type: 'GO' or 'EC'
  • branch: 'MF', 'BP' or 'CC' if dataset_type is 'GO'
  • freeze: True or False (freezing GCN layer weights)

The script can be run from the command line or in a bash script like this:

python training_downstream.py

Embedding Space Visualization

There are two notebooks that demonstrate how to visualize the predicted embedding space.

  • visualize_predicted.ipynb: Colors predicted embedding space by molecular function and biological process
  • visualize_by_structure.ipynb: Colors predicted embedding space by CATH superfamilies

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published