https://github.com/carrascomj/gcn-prot

Graph convolutional networks for structural learning of proteins
https://github.com/carrascomj/gcn-prot

deep-learning gcn gcnn graph-convolutional-networks proteins

Last synced: 4 months ago
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Graph convolutional networks for structural learning of proteins

• Host: GitHub
• URL: https://github.com/carrascomj/gcn-prot
• Owner: carrascomj
• License: mit
• Created: 2020-03-05T13:24:27.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-19T17:33:57.000Z (over 5 years ago)
• Last Synced: 2025-06-04T09:06:19.133Z (5 months ago)
• Topics: deep-learning, gcn, gcnn, graph-convolutional-networks, proteins
• Language: Jupyter Notebook
• Size: 20.4 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
gcn-prot

==============================

[![Build Status](https://travis-ci.com/carrascomj/gcn-prot.svg?branch=master)](https://travis-ci.com/carrascomj/gcn-prot)

[![Coverage](https://codecov.io/gh/carrascomj/gcn-prot/branch/master/graph/badge.svg)](https://codecov.io/gh/carrascomj/gcn-prot)

[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

Graph convolutional networks to perform structural learning of proteins. The

starting point is the work of [Zamora-Resendiz and Crivelli, 2019](https://www.biorxiv.org/content/10.1101/610444v1.full)

(repository on [Github](https://github.com/CrivelliLab/Protein-Structure-DL)).

Authors

-------

Bjorn Hansen  

Jorge Carrasco Muriel (@carrascomj)

This project was developed for the Advance Machine Learning course at DTU.

Project Organization

--------------------

    ├── LICENSE

    ├── Makefile           <- Makefile with commands like `make data` or `make train`

    ├── README.md          <- The top-level README for developers using this project.

    ├── data

    │   ├── external       <- Data from third party sources.

    │   ├── interim        <- Intermediate data that has been transformed.

    │   ├── processed      <- The final, canonical data sets for modeling.

    │   └── raw            <- The original, immutable data dump.

    │

    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details

    │

    ├── models             <- Trained and serialized models, model predictions, or model summaries

    │

    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),

    │                         the creator's initials, and a short `-` delimited description, e.g.

    │                         `1.0-jqp-initial-data-exploration`.

    │

    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.

    │

    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.

    │   └── figures        <- Generated graphics and figures to be used in reporting

    │

    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.

    │                         generated with `pip freeze > requirements.txt`

    │

    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported

    ├── src                <- Source code for use in this project.

    │   ├── __init__.py    <- Makes src a Python module

    │   │

    │   ├── data           <- Scripts to download or generate data

    │   │   └── make_dataset.py

    │   │

    │   ├── features       <- Scripts to turn raw data into features for modeling

    │   │   └── build_features.py

    │   │

    │   ├── models         <- Scripts to train models and then use trained models to make

    │   │   │                 predictions

    │   │   ├── predict_model.py

    │   │   └── train_model.py

    │   │

    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations

    │       └── visualize.py

    │

    └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

--------

Project based on the cookiecutter data science project template. #cookiecutterdatascience