Network biomarkers reveal dysfunctional gene regulations during disease progression

Tao Zeng, Shao Yan Sun, Yong Wang, Hailong ZHU, Luonan Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

Extensive studies have been conducted on gene biomarkers by exploring the increasingly accumulated gene expression and sequence data generated from high-throughput technology. Here, we briefly report on the state-of-the-art research and application of biomarkers from single genes (i.e. gene biomarkers) to gene sets (i.e. group or set biomarkers), gene networks (i.e. network biomarkers) and dynamical gene networks (i.e. dynamical network biomarkers). In particular, differential and dynamical network biomarkers are used as representative examples to demonstrate their effectiveness in both detecting early signals for complex diseases and revealing essential mechanisms on disease initiation and progression at a network level. Here, we briefly report on the state-of-the-art research and application of biomarkers from single genes to gene sets, gene networks and dynamical gene networks, which explore the increasingly-accumulated gene expression and sequence data. Differential network biomarkers and dynamical network biomarkers are used as representative examples to demonstrate their effectiveness on detecting early signals for complex diseases and revealing essential pathogen mechanisms.

Original languageEnglish
Pages (from-to)5682-5695
Number of pages14
JournalFEBS Journal
Volume280
Issue number22
DOIs
Publication statusPublished - Nov 2013

Scopus Subject Areas

  • Biochemistry
  • Molecular Biology
  • Cell Biology

User-Defined Keywords

  • differential expression network
  • disease diagnosis
  • disease prognosis
  • disease progression
  • dynamical network biomarkers
  • gene regulation
  • module biomarkers
  • network biomarkers
  • progressive module network model
  • systems biology

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