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Related Experiment Video

Updated: Nov 4, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.

Jiang Xie1, Fuzhang Yang1, Jiao Wang2

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Neurocomputing
|May 24, 2021
PubMed
Summary

We developed differential network flow (DNF) to identify key gene regulators in development and disease. DNF captures global network changes, outperforming existing methods in identifying driver genes across diverse biological datasets.

Keywords:
differential network analysisinformation entropynetwork flownetwork topologyneuronal differentiation

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Differential network analysis is crucial for identifying driver genes in biological processes and diseases.
  • Current methods often focus on local network features, making them susceptible to noise and masking true regulatory activity.
  • A need exists for methods that can distinguish genuine regulatory elements from stochastic variations and downstream effects.

Purpose of the Study:

  • To introduce the differential network flow (DNF) method for identifying key regulators in developmental and disease progression.
  • To develop a method capable of quantifying node essentiality by analyzing differences in network flow distributions across biological states.
  • To capture comprehensive topological differences, from local to global network features.

Main Methods:

  • The differential network flow (DNF) method was proposed.
  • DNF quantifies node essentiality by analyzing differences in network flow distributions between consecutive biological states.
  • The method was applied to human cancer genomics data and murine single-cell RNA sequencing data.

Main Results:

  • DNF demonstrated more accurate driver-gene identification compared to state-of-the-art methods.
  • The method was successfully applied to The Cancer Genome Atlas (TCGA) datasets and single-cell RNA-seq data from neural and hematopoietic differentiation.
  • DNF predicted key regulators of network crosstalk in neuronal differentiation and neurodegenerative disease, identifying APP as a driver gene in neural stem cell differentiation.

Conclusions:

  • Differential network flow (DNF) offers a novel approach for quantifying gene essentiality across networks representing different biological states.
  • DNF effectively separates true regulatory impacts from noise and downstream effects, enhancing driver-gene identification accuracy.
  • The method provides insights into complex biological progressions, including differentiation and disease, by analyzing network dynamics.