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A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

Jiang G, Wang C, Zhu Q, Chute CG - AMIA Jt Summits Transl Sci Proc (2013)

Bottom Line: We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework.We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach.We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.

ABSTRACT
Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

No MeSH data available.


Related in: MedlinePlus

System architecture
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f1-amia_tbi_2013_088: System architecture

Mentions: Figure 1 shows our system architecture of knowledge integration for pharmacogenomics knowledge discovery applications in a semantic web-based framework. In the Semantic Normalization layer, 1) we transform and represent the ADE knowledge in a RDF based data model; 2) we utilize two RDF graphs in a Semantic MEDLINE RDF store developed in our previous study. The two RDF graphs represent the domain patterns for the associations of disease-gene and drug-gene.


A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

Jiang G, Wang C, Zhu Q, Chute CG - AMIA Jt Summits Transl Sci Proc (2013)

System architecture
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814489&req=5

f1-amia_tbi_2013_088: System architecture
Mentions: Figure 1 shows our system architecture of knowledge integration for pharmacogenomics knowledge discovery applications in a semantic web-based framework. In the Semantic Normalization layer, 1) we transform and represent the ADE knowledge in a RDF based data model; 2) we utilize two RDF graphs in a Semantic MEDLINE RDF store developed in our previous study. The two RDF graphs represent the domain patterns for the associations of disease-gene and drug-gene.

Bottom Line: We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework.We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach.We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.

ABSTRACT
Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

No MeSH data available.


Related in: MedlinePlus