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Semantics based approach for analyzing disease-target associations.

Rama Kaalia1, Indira Ghosh1

  • 1School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

Journal of Biomedical Informatics
|June 29, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a semantic web-based approach to integrate complex disease information, developing the Disease Association Ontology for Diabetes (DAO-db). This ontology aids in identifying potential diabetes targets by analyzing gene-disease associations.

Keywords:
Disease genesFunctional interactionsInformation integrationInformation representationOntologySemantic web

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genomics

Background:

  • Complex diseases arise from intricate interactions between genes, their products, and environmental factors.
  • Understanding disease etiology requires integrating diverse data using experimental, statistical, and computational methods.
  • A key challenge lies in representing and integrating heterogeneous biomedical information for complex diseases.

Purpose of the Study:

  • To address the challenge of representing and integrating heterogeneous biomedical information for complex diseases.
  • To develop a semantics-based approach for disease association analysis.
  • To create a standardized platform for diabetes-related gene and pathway information.

Main Methods:

  • Utilized semantic web technology to design the Disease Association Ontology for Diabetes (DAO-db).
  • Integrated functional associations of disease genes using RDF graphs within DAO-db.
  • Applied semantic web-based scoring algorithms (PageRank, HITS) to analyze gene interactions.

Main Results:

  • DAO-db offers a standardized ontology-driven platform for diabetes-related genes, proteins, and pathways.
  • Integrated functional associations across multiple interaction levels (gene-disease, gene-pathway, etc.).
  • Developed an automatic instance loader for large-scale data addition to DAO-db.

Conclusions:

  • The developed ontology provides a framework for querying and analyzing disease-associated information via RDF graphs.
  • The methodology enables the prediction of novel potential therapeutic targets for diabetes.
  • Facilitates the analysis of gene-disease associations to identify key players in disease pathogenesis.