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Robust gene coexpression networks using signed distance correlation.

Javier Pardo-Diaz1,2, Lyuba V Bozhilova1, Mariano Beguerisse-Díaz3

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This study introduces signed distance correlation to build gene coexpression networks, improving stability and biological insight without needing prior functional annotations. The new method offers a more intuitive and robust approach for gene network analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Many genes lack functional annotations, hindering biological relationship inference.
  • Gene coexpression networks are valuable for discovering gene functions but require reliable validation.
  • Existing network construction methods face challenges due to annotation scarcity.

Purpose of the Study:

  • To develop a principled method for constructing structurally stable gene coexpression networks.
  • To infer biological relationships between genes using only gene expression data.
  • To overcome limitations of current methods in the absence of functional information.

Main Methods:

  • Introduced signed distance correlation as a novel measure of variable dependency.
  • Applied signed distance correlation to generate gene coexpression networks.
  • Developed a framework for self-consistent network generation purely from gene expression data.

Main Results:

  • Gene coexpression networks generated using signed distance correlation are more stable.
  • The proposed method captures more biological information compared to Pearson correlation or mutual information.
  • Networks are robust and informative even without prior functional annotations.

Conclusions:

  • Signed distance correlation provides a powerful and intuitive approach for gene coexpression network construction.
  • This method enhances the reliability of gene function inference by generating stable and informative networks.
  • The framework offers a valuable tool for systems biology research, particularly when functional annotations are limited.