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Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Hypergraphs and centrality measures identifying key features in gene expression data.

Samuel Barton1, Zoe Broad2, Daniel Ortiz-Barrientos2

  • 1School of Mathematics and Physics, ARC Centre of Excellence, Plant Success in Nature and Agriculture, University of Queensland, Brisbane, 4072, Australia.

Mathematical Biosciences
|November 1, 2023
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Summary
This summary is machine-generated.

This study introduces hypergraphs to analyze multi-way gene interactions in expression data, offering new insights into complex biological systems. This approach identifies key genes by revealing dominant interactions, advancing systems biology research.

Keywords:
Gene expressionGraph TheoryHypergraph theoryMathematical modelling

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Area of Science:

  • Systems Biology
  • Graph Theory
  • Bioinformatics

Background:

  • Gene co-expression networks model pairwise gene interactions, integrating biological knowledge with graph theory.
  • Complex biological systems involve intricate multi-way interactions often missed by traditional network analysis.

Purpose of the Study:

  • To introduce hypergraphs as a novel tool for analyzing multi-way interactions in gene expression data.
  • To extend existing network analysis methods by incorporating higher-order interactions.

Main Methods:

  • Representing individual genes as hyperedges and features as vertices within a hypergraph framework.
  • Utilizing line graph representations to simplify dense hypergraphs and enable graph centrality measures.
  • Applying graph centrality measures to identify significant hyperedges (hub genes) within the gene expression data.

Main Results:

  • Demonstrated the utility of hypergraphs in capturing complex, multi-way gene interactions.
  • Successfully identified dominant or hub-like hyperedges, corresponding to key genes in the dataset.
  • Validated the hypergraph approach using gene expression data from the plant species Senecio lautus.

Conclusions:

  • Hypergraphs provide a powerful framework for dissecting multi-way interactions in gene expression data.
  • This method enhances the identification of crucial genes and biological pathways.
  • The approach offers a significant advancement in systems biology and bioinformatics analysis.