TigerLily: Finding drug interactions in silico with the Graph

04/18/2022
by   Benedek Rozemberczki, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/19/2022

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Co-administration of two or more drugs simultaneously can result in adve...
research
08/30/2022

Graph Distance Neural Networks for Predicting Multiple Drug Interactions

Since multidrug combination is widely applied, the accurate prediction o...
research
03/11/2019

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures

Drug-drug interactions are preventable causes of medical injuries and of...
research
07/12/2022

DDI Prediction via Heterogeneous Graph Attention Networks

Polypharmacy, defined as the use of multiple drugs together, is a standa...
research
10/12/2020

Point Process Modeling of Drug Overdoses with Heterogeneous and Missing Data

Opioid overdose rates have increased in the United States over the past ...
research
08/05/2022

NRBdMF: A recommendation algorithm for predicting drug effects considering directionality

Predicting the novel effects of drugs based on information about approve...
research
04/15/2020

Wasserstein Adversarial Autoencoders for Knowledge Graph Embedding based Drug-Drug Interaction Prediction

Interaction between pharmacological agents can trigger unexpected advers...

Please sign up or login with your details

Forgot password? Click here to reset