SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

ABSTRACT:

Motivation

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 existing approaches ignore KGs altogether. Some tries 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 mo

SUBMITTER: Yu Y 

PROVIDER: S-EPMC10060701 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature