SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge
Graph Summarization
- URL: http://arxiv.org/abs/2010.01450v2
- Date: Thu, 6 May 2021 21:07:42 GMT
- Title: SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge
Graph Summarization
- Authors: Yue Yu, Kexin Huang, Chao Zhang, Lucas M. Glass, Jimeng Sun, and Cao
Xiao
- Abstract summary: We propose SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module.
SumGNN outperforms the best baseline by up to 5.54%, and the performance gain is particularly significant in low data relation types.
- Score: 64.56399911605286
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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