Semantic Inference for Pharmacokinetic Drug-Drug Interactions

Moitra, Abha; Palla, Ravi; Tari, Luis; Krishnamoorthy, Mukkai S.

doi:10.1109/icsc.2014.36

Cited by 7 publications

(10 citation statements)

References 16 publications

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“…In this study, we have demonstrated that the use of the semantic web standard languages OWL and SWRL support the formal representation of complex pharmacological knowledge. In addition, we have demonstrated that the combination of drug-related facts in our ontology and inference rules representing different DDI mechanisms can be used to infer DDIs and their mechanisms in a larger scale than previous projects. ,,,, …”

Section: Discussionmentioning

confidence: 93%

“…Boyce et al demonstrated that a set of rules in first-order logic could represent how one drug alters the metabolism of another drug. The same PK DDI mechanism was represented by Tari et al and Moitra et al using the logic programming language ASP . However, logic programming has not been designed as an ontology language for direct interchange of knowledge, which hinders interoperability with the remainder of the semantic web and ontologies .…”

Section: Methodsmentioning

confidence: 99%

“…It can be divided into two main subtypes: those using semantic networks and those based on description logics (DLs) representations and/or rules. Semantic networks represent drugs as nodes and interactions between drugs as edges, and have been used to infer DDIs between other drugs through reasoning. , Recently, there is an increasing interest in more expressive representation formalisms capable of supporting additional inferences, which have proven to be useful to predict DDIs on the basis of their underlying mechanisms. ,,,, However, none of the existing projects have dealt with the inference of DDIs occurring by different mechanisms within the same framework. DDIs are broadly classified into two main groups with respect to their mechanisms: pharmacokinetic (PK) and pharmacodynamic (PD) DDIs.…”

Section: Introductionmentioning

confidence: 99%

“…A PK DDI occurs when one drug affects the levels of another drug in the body, while a PD DDI occurs when one drug alters the effects of another drug without altering its concentration. While only one project dealt with the representation and prediction of PD DDIs, the remaining projects focused on PK DDIs ,,, and specifically on metabolism-related DDIs.…”

Section: Introductionmentioning

confidence: 99%

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

Herrero-Zazo

Segura-Bedmar

Hastings

et al. 2015

J. Chem. Inf. Model.

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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. The ontology is available from https://code.google.com/p/dinto/.

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Section: Discussionmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Herrero-Zazo

Segura-Bedmar

Hastings

et al. 2015

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…In the field of drug-drug interaction (DDI), there are many studies using inference rules to detect new relations in knowledge graph, and we referred their evaluation methods in this paper. Moitra et al [17] modeled the DDI of pharmacokinetic using Semantic Application Design Language, then estimated interactions related to several enzymes using SWI-Prolog. Herrero-Zazo et al [18] provided a comprehensive ontology for interactions between pharmacokinetic and pharmacodynamic (DINTO), then estimated the relations such as "may interact with" using the DINTO and SWRL rules.…”

Section: Semantic Inference To Detect Relationsmentioning

confidence: 99%

Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules

et al. 2022

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Urban areas have many problems, including homelessness, graffiti, and littering. These problems are influenced by various factors and are linked to each other; thus, an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles. Moreover, before implementing action plans to solve these problems, local governments need to estimate cost-effectiveness when the plans are carried out. Therefore, this paper proposes constructing an urban problem knowledge graph that would include urban problems' causality and the related cost information in budget sheets. In addition, this paper proposes a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph. Finally, several root problems that led to vicious cycles were detected. Urban-problem experts evaluated the extracted causal relations.

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Drug-Drug Interactions Prediction Based on Similarity Calculation and Pharmacokinetics Mechanism

Zhang

Chen

et al. 2018

Smart Health

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Semantic Inference for Pharmacokinetic Drug-Drug Interactions

Cited by 7 publications

References 16 publications

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

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

Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules

Drug-Drug Interactions Prediction Based on Similarity Calculation and Pharmacokinetics Mechanism

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