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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Chapter 9: Analyses using disease ontologies.

Nigam H Shah1, Tyler Cole, Mark A Musen

  • 1Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States of America. nigam@stanford.edu

Plos Computational Biology
|January 10, 2013
PubMed
Summary
This summary is machine-generated.

Enrichment analysis, commonly using Gene Ontology (GO), identifies significant genes from high-throughput data. This method can be generalized to other ontologies, including disease terms, for broader biological insights.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput data analysis generates extensive lists of significant genes.
  • Gene Ontology (GO) enrichment analysis is a standard method to interpret these gene lists by identifying over- or under-represented biological processes, molecular functions, or cellular components.

Purpose of the Study:

  • To discuss the generalization of enrichment analysis methods beyond Gene Ontology (GO).
  • To explore the application of enrichment analysis using disease ontologies for translational research.

Main Methods:

  • Reviewing the general methodology of enrichment analysis.
  • Discussing the use of disease ontologies and public computational infrastructure for novel analyses.
  • Highlighting the aggregation of ontology concepts for gene sets.

Main Results:

  • Enrichment analysis is a versatile tool applicable to various ontologies, not just GO.
  • Disease ontologies can be used to identify over-represented disease classes within gene sets.
  • Novel translational analyses are enabled by integrating disease ontologies with public data.

Conclusions:

  • Enrichment analysis methods are generalizable and can be applied to any biomedical ontology.
  • Utilizing disease ontologies expands the scope of enrichment analysis, facilitating translational research.
  • The integration of disease ontologies with computational infrastructure offers powerful new avenues for biological discovery.