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

Disease pathways in the human interactome
https://github.com/mims-harvard/pathways

interactome molecular-biology network-analysis network-science pathway-analysis pathway-prediction

Last synced: 8 months ago
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Disease pathways in the human interactome

• Host: GitHub
• URL: https://github.com/mims-harvard/pathways
• Owner: mims-harvard
• License: mit
• Created: 2018-02-12T18:38:56.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-03-20T16:55:12.000Z (about 8 years ago)
• Last Synced: 2024-03-26T02:22:12.097Z (about 2 years ago)
• Topics: interactome, molecular-biology, network-analysis, network-science, pathway-analysis, pathway-prediction
• Language: Python
• Homepage: http://snap.stanford.edu/pathways
• Size: 1.46 MB
• Stars: 21
• Watchers: 5
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Pathways

We perform a large-scale analysis of *disease pathways* in the human *interactome* to better understand connectivity and higher-order network structure of disease pathways.

Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment.

![Examples of disease pathways](/images/disease-pathway-examples.png)

Here we study the PPI network structure of disease pathways. We find that 90% of pathways do not correspond to single well-connected components in the PPI network. Instead, proteins associated with a single disease tend to form many separate connected components/regions in the network. We then evaluate state-of-the-art disease pathway discovery methods and show that their performance is especially poor on diseases with disconnected pathways.

![Disease pathway discovery](/images/disease-pathway-discovery.png)

We conclude that network connectivity structure alone may not be sufficient for disease pathway discovery. However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.

Please check the [project website](http://snap.stanford.edu/pathways/) for more details. 

  

## Citing

If you find disease pathway analysis useful for your research, please consider citing:

    @inproceedings{agrawal2018,

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

      title = {Large-scale Analysis of Disease Pathways in the Human Interactome},

      year = {2018},

      booktitle = {Pacific Symposium on Biocomputing},

      volume = {23},

      pages = {111-122}

    }

## Miscellaneous

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

algorithm to .

## License

Decagon is licensed under the MIT License.