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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Statistical tests for associations between two directed acyclic graphs.

Robert Hoehndorf1, Axel-Cyrille Ngonga Ngomo, Michael Dannemann

  • 1Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany. hoehndor@ebi.ac.uk

Plos One
|June 30, 2010
PubMed
Summary
This summary is machine-generated.

We developed statistical tests to find links between biological data represented as directed acyclic graphs (DAGs). This method uncovers hidden associations in biomedical ontologies, revealing biologically relevant connections.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Biological data and annotations are increasingly structured using directed acyclic graphs (DAGs).
  • Existing DAG representations are often isolated, hindering the discovery of cross-domain biological relationships.
  • Identifying links between distinct biological domains is a significant challenge.

Purpose of the Study:

  • To develop novel statistical tests for discovering strong associations between two directed acyclic graphs (DAGs).
  • To address the challenge of identifying implicit biological information across unconnected DAGs.
  • To enable the extraction of biologically relevant relations between biomedical ontologies.

Main Methods:

  • A novel family of general statistical tests was developed.
  • The method considers graph topology and the specificity and relevance of node associations.
  • The approach was applied to extract associations between biomedical ontologies.

Main Results:

  • The developed statistical tests successfully identified strong associations between DAGs.
  • Application to biomedical ontologies revealed biologically relevant relations.
  • Both manual and automatic evaluations confirmed the discovery of meaningful connections.

Conclusions:

  • The novel statistical tests provide a robust method for discovering associations between DAGs.
  • This approach facilitates the integration and interpretation of biological data across different domains.
  • The implemented suite of tests is freely available for use in biological research.