https://github.com/mims-harvard/decagon

Graph convolutional neural network for multirelational link prediction
https://github.com/mims-harvard/decagon

deep-learning embeddings graph-convolutional-networks graph-neural-networks pharmacology representation-learning

Last synced: 5 months ago
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Graph convolutional neural network for multirelational link prediction

• Host: GitHub
• URL: https://github.com/mims-harvard/decagon
• Owner: mims-harvard
• License: mit
• Created: 2018-01-31T20:49:12.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2022-11-21T22:25:56.000Z (over 3 years ago)
• Last Synced: 2024-05-21T17:23:19.263Z (almost 2 years ago)
• Topics: deep-learning, embeddings, graph-convolutional-networks, graph-neural-networks, pharmacology, representation-learning
• Language: Jupyter Notebook
• Homepage: http://snap.stanford.edu/decagon
• Size: 605 KB
• Stars: 438
• Watchers: 24
• Forks: 142
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Decagon: Representation Learning on Multimodal Graphs

#### Author: [Marinka Zitnik](http://stanford.edu/~marinka) (marinka@cs.stanford.edu)

#### [Project website](http://snap.stanford.edu/decagon)

## Overview

This repository contains code necessary to run the Decagon algorithm. Decagon is a method for learning node 

embeddings in multimodal graphs, and is especially useful for link prediction in highly multi-relational settings. See 

our [paper](https://doi.org/10.1093/bioinformatics/bty294) for details on the algorithm.

  

## Usage: Polypharmacy

Decagon is used to address a burning question in pharmacology, which is that of predicting 

[safety of drug combinations](http://stanford.edu/~marinka/slides/decagon-ismb18.pdf). 

We construct a multimodal graph of protein-protein interactions, drug-protein target interactions, and 

polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a 

different type. 







Decagon uses graph convolutions to embed the multimodal graph in a compact vector space and then uses

the learned embeddings to predict side effects of drug combinations. 

  







### Running the code

The setup for the polypharmacy problem on a synthetic dataset is outlined in `main.py`. It uses a small synthetic 

network example with five edge types. Run the code as following:

    $ python main.py

    

The full polypharmacy dataset (described in the paper) is available on the 

[project website](http://snap.stanford.edu/decagon). To run the code on the full dataset first download all data files

from the [project website](http://snap.stanford.edu/decagon). The polypharmacy dataset is already preprocessed and ready to use. 

After cloning the project, replace the synthetic example in `main.py` with the polypharmacy dataset and run the model.  

## Citing

If you find *Decagon* useful for your research, please consider citing [this paper](https://academic.oup.com/bioinformatics/article/34/13/i457/5045770):

    @article{Zitnik2018,

      title     = {Modeling polypharmacy side effects with graph convolutional networks.},

      author    = {Zitnik, Marinka and Agrawal, Monica and Leskovec, Jure},

      journal   = {Bioinformatics},

      volume    = {34},

      number    = {13},

      pages     = {457–466},

      year      = {2018}

    }

## Miscellaneous

Please send any questions you might have about the code and/or the 

algorithm to .

This code implements several different edge decoders (innerproduct, distmult, 

bilinear, dedicom) and loss functions (hinge loss, cross entropy). Many deep variants are possible and what works 

best might depend on a concrete use case.  

## Requirements

Decagon is tested to work under Python 2 and Python 3. 

Recent versions of Tensorflow, sklearn, networkx, numpy, and scipy are required. All the required packages can be installed using the following command:

    $ pip install -r requirements.txt

## License

Decagon is licensed under the MIT License.