Title
DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug-Drug Interactions and Their Mechanisms.
Abstract
The early detection of drug drug interactions (DDIs) is limited by the diffuse spread of DDI information in heterogeneous sources. Computational methods promise to play a key role in the identification and explanation of DDIs on a large scale. However, such methods rely on the availability of computable representations describing the relevant domain knowledge. Current modeling efforts have focused on partial and shallow representations of the DDI domain, failing to adequately support computational inference and discovery applications. In this paper, we describe a comprehensive ontology for DDI knowledge (DINTO), which is the first formal representation of different types of DDIs and their mechanisms and its application in the prediction of DDIs. This project has been developed using currently available semantic web technologies, standards, and tools, and we have demonstrated that the combination of drug-related facts in DINTO and Semantic Web Rule Language (SWRL) rules can be used to infer DDIs and their different mechanisms on a large scale.
Year
DOI
Venue
2015
10.1021/acs.jcim.5b00119
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Field
DocType
Volume
Ontology (information science),Data mining,Ontology,Information retrieval,Domain knowledge,Computer science,Inference,Semantic Web,Bioinformatics,Semantic Web Rule Language,Semantics,The Internet
Journal
55
Issue
ISSN
Citations 
8
1549-9596
11
PageRank 
References 
Authors
0.60
24
4
Name
Order
Citations
PageRank
María Herrero-Zazo1884.76
Isabel Segura-Bedmar243530.96
Janna Hastings371462.06
Paloma Martínez471785.63