TigerLily Documentation

TigerLily is a TigerGraph-based system designed to solve the drug interaction prediction task. In this machine learning task, we want to predict whether two drugs have an adverse interaction. Our framework allows us to solve this highly relevant real-world problem using graph mining techniques in these steps:

• 1. Using PyTigergraph we create a heterogeneous biological graph of drugs and proteins.

• 2. We calculate the personalized PageRank scores of drug nodes in the TigerGraph Cloud.

• 3. We embed the nodes using sparse non-negative matrix factorization of the personalized PageRank matrix.

• 4. Using the node embeddings we train a gradient boosting based drug interaction predictor.

If you would use TigerLily please cite our work:

>@misc{tigerlily2022,
  author = {Benedek Rozemberczki},
  title = {TigerLily: Finding drug interactions insilico with the Graph},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/benedekrozemberczki/tigerlily}},
}

Package Reference

• Tigerlily
  • Tigerlily API
  • Functions
  • Classes
  • Class Inheritance Diagram

Notes

• Installation
• Tutorial
  • Creating and Populating a Graph
  • Computing the Approximate Personalized PageRank Vectors
  • Learning the Drug Embeddings and Drug Pair Feature Generation
  • Predicting Drug Interactions and Inference