Network biomarkers reveal dysfunctional gene regulations during disease progression
- 22 October 2013
- journal article
- review article
- Published by Wiley in The FEBS Journal
- Vol. 280 (22), 5682-5695
- https://doi.org/10.1111/febs.12536
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.Keywords
This publication has 78 references indexed in Scilit:
- Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network BiomarkersMedicinal Research Reviews, 2013
- Clinical Relevance of Plasma Prostaglandin F2α Metabolite Concentrations in Patients with Idiopathic Pulmonary FibrosisPLOS ONE, 2013
- Differential network biologyMolecular Systems Biology, 2012
- Algorithms in nature: the convergence of systems biology and computational thinkingMolecular Systems Biology, 2011
- An Integrated Approach to Uncover Drivers of CancerCell, 2010
- Mixture classification model based on clinical markers for breast cancer prognosisArtificial Intelligence in Medicine, 2010
- A Gene Signature Predictive for Outcome in Advanced Ovarian Cancer Identifies a Survival Factor: Microfibril-Associated Glycoprotein 2Cancer Cell, 2009
- Metabolic brain networks in neurodegenerative disorders: a functional imaging approachTrends in Neurosciences, 2009
- Tissue- and age-specific changes in gene expression during disease induction and progression in NOD miceClinical Immunology, 2008
- Gene expression profiling predicts clinical outcome of breast cancerNature, 2002