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Updated: Aug 12, 2025

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WMDS.net: a network control framework for identifying key players in transcriptome programs.

Xiang Cheng1,2, Md Amanullah2,3, Weigang Liu3

  • 1Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310006, China.

Bioinformatics (Oxford, England)
|February 2, 2023
PubMed
Summary
This summary is machine-generated.

Identifying key gene regulators is crucial for understanding cellular phenotype transitions. A new model, WMDS.net, effectively finds these critical driver nodes in transcriptional networks, aiding in disease gene discovery.

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Area of Science:

  • Systems biology
  • Computational biology
  • Genomics

Background:

  • Mammalian cells exhibit inherent controllability in transcriptional networks, allowing phenotype transitions.
  • Identifying key regulators is essential for understanding and manipulating these complex cellular state changes.
  • Existing methods may not fully capture the nuances of network controllability for identifying critical driver genes.

Purpose of the Study:

  • To propose a novel computational model, WMDS.net, for identifying key regulators in transcriptional co-expression networks.
  • To leverage network controllability theory to pinpoint driver nodes that can fully control network state transitions.
  • To apply the model for discovering cancer driver genes using transcriptomic data.

Main Methods:

  • Developed a weighted Minimum Dominating Set (WMDS.net) network model based on structural controllability theory.
  • Integrated node degree and differential gene co-expression significance to measure node controllability.
  • Applied WMDS.net to analyze RNA-seq datasets from The Cancer Genome Atlas (TCGA) for cancer driver gene discovery.

Main Results:

  • WMDS.net successfully identified key driver nodes in differential gene co-expression networks.
  • The model demonstrated robust performance across various cancer datasets.
  • WMDS.net outperformed existing top-tier tools in cancer driver gene discovery, showing improved precision and recall.

Conclusions:

  • WMDS.net provides a powerful framework for identifying critical regulators in transcriptional networks.
  • The model offers valuable insights into network state transitions underlying cellular phenotypes.
  • WMDS.net is a promising tool for advancing cancer driver gene discovery and potentially other complex biological studies.