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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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DNLC: differential network local consistency analysis.

Jianwei Lu1,2, Yao Lu1, Yusheng Ding1

  • 1School of Software Engineering, Tongji University, Shanghai, China.

BMC Bioinformatics
|December 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Differential Network Local Consistency (DNLC), a new method to identify gene expression changes in biological networks. DNLC effectively detects significant shifts in gene modules between clinical conditions.

Keywords:
Biological networkGene expressionLocal Moran’s I

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Biological networks are dynamic, with gene functional relations changing based on biological conditions.
  • Analyzing subnetworks with altered expression consistency can reveal regulatory mechanisms in diseases or development.

Purpose of the Study:

  • To develop a novel computational method for identifying genes and modules exhibiting significant changes in local expression consistency between clinical conditions.
  • To provide a tool that complements traditional differential expression analysis for biological discoveries.

Main Methods:

  • Developed a new method named Differential Network Local Consistency (DNLC).
  • DNLC selects genes and modules based on significant changes in local expression consistency across different biological states.
  • Validated the method using simulations and two real-world gene expression datasets.

Main Results:

  • DNLC effectively detected artificially introduced local consistency changes in simulations.
  • The method identified novel genes and network modules with biological relevance in applied datasets.
  • Demonstrated the method's capability to pinpoint subnetworks with altered expression consistency between clinical conditions.

Conclusions:

  • Differential Network Local Consistency (DNLC) is an effective tool for identifying gene expression modules that change between clinical conditions.
  • DNLC complements existing differential expression analyses, enhancing the discovery of biological insights from gene expression data.
  • An R package for DNLC is publicly available for broader research application.