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emmatysinger committed Sep 26, 2024
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2 changes: 2 additions & 0 deletions .gitattributes
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# Auto detect text files and perform LF normalization
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136 changes: 136 additions & 0 deletions .gitignore
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slurm-*

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
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lib/
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MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
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MultiviewContrast_ECToy.pth
21 changes: 21 additions & 0 deletions LICENSE
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MIT License

Copyright (c) 2024 Emma Tysinger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
40 changes: 40 additions & 0 deletions README.md
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# GearNet-ProtGNN: Protein function prediction model from protein structure and biomedical knowledge

This repository hosts the official implementation of 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



146 changes: 146 additions & 0 deletions environment.yml
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name: gearnetenv
channels:
- conda-forge
- defaults
dependencies:
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