DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug鈥揇rug Interactions and Their Mechanisms

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• 作者：Mar铆a Herrero-Zazo ; Isabel Segura-Bedmar ; Janna Hastings ; Paloma Mart铆nez
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2015
• 出版时间：August 24, 2015
• 年：2015
• 卷：55
• 期：8
• 页码：1698-1707
• 全文大小：429K
• ISSN：1549-960X

文摘

The early detection of drug鈥揹rug 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. The ontology is available from https://code.google.com/p/dinto/.