mlsanityData to 7 Oct 2026

TigerLily: Finding drug interactions in silico with the Graph

arXiv 2204.08206 ↗First posted 18 Apr 2022cs.LG cs.AI cs.SIcited by 0

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Abstract (arXiv)

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: (a) Using PyTigergraph we create a heterogeneous biological graph of drugs and proteins. (b) We calculate the personalized PageRank scores of drug nodes in the TigerGraph Cloud. (c) We embed the nodes using sparse non-negative matrix factorization of the personalized PageRank matrix. (d) Using the node embeddings we train a gradient boosting based drug interaction predictor.

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References · 20

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8 more
  1. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 152
  2. 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
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  7. 2021Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphscited by 7
  8. 2021MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapycited by 6

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