SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization

Abstract

Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g., experimental data). Most of the existing approaches ignore KGs altogether. Some try to directly integrate KGs with other data via graph neural networks with limited success. Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task. To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate a reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54\%, and the performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction.

Published in Bioinformatics 2021

References in corpus (6)

• LINE: Large-scale Information Network Embedding
• KGAT: Knowledge Graph Attention Network for Recommendation
• Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
• STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
• Subgraph Neural Networks
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification

Cited by in corpus (9)

• Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
• Graph Neural Networks for Molecules
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
• Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
• K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
• Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
• Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
• R-Mixup: Riemannian Mixup for Biological Networks
• Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey