http://dx.doi.org/10.1111/febs.12536">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Network biomarkers reveal dysfunctional gene regulations during disease progression

Language

English

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. © 2013 FEBS.

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

Publication Date

2013

Source Publication Title

FEBS Journal

Volume

280

Issue

22

Start Page

5682

End Page

5695

Publisher

Wiley

ISSN (print)

1742464X

ISSN (electronic)

17424658

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