Paper Info
Title
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding.
Abstract
Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs but ignore other interactions. In this paper, we propose a novel framework, called PRD, to predict drug-drug interactions. The framework uses the graph embedding that can overcome data incompleteness and sparsity issues to achieve multiple DDI label prediction. First, a large-scale drug knowledge graph is generated from different sources. Then, the knowledge graph is embedded with comprehensive biomedical text into a common low dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world datasets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.
Year
Venue
Field
2017
arXiv: Artificial Intelligence
Data mining,Knowledge graph,Embedding,Computer science,Graph embedding,Trustworthiness,Artificial intelligence,Big data,Machine learning,Binary number
DocType
Volume
Citations 
Journal
abs/1712.08875
0
PageRank 
References 
Authors
0.34
16
7
Name
Order
Citations
PageRank
Meng Wang12088.38
Yihe Chen232.06
Buyue Qian330.73
Jun Liu418040.96
Sen Wang547737.24
Guodong Long665547.27
Fei Wang724151.35