A collection of papers and resources about Dynamic Network Biomarkers (DNB) and its related algorithms.
DNB is a novel concept designed to capture the dynamic changes of complex system on the progress of disease transitions. Unlike traditional biomarkers that only measure static levels of molecular activity, DNB can reveal the critical state and the driving networks of diseases before clinical manifestations. DNB and related algorithms have been successfully applied to various diseases such as cancer, diabetes, and Alzheimer's disease.
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Nonlinear oscillations, dynamical systems, and bifurcations of vector fields (Springer, 1983)
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Slowing down as an early warning signal for abrupt climate change (PNAS, 2008)
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Early-warning signals for critical transitions (Nature, 2009)
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Anticipating Critical Transitions (Science, 2012)
- Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach (BMC Genomics, 2011)
- Identifying disease genes and module biomarkers by differential interactions (Journal of the American Medical Informatics Association, 2012)
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Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers (Scientific Reports, 2012)
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Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers (Medicinal Research Reviews, 2013)
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Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers (BMC Medical Genomics, 2013)
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Personalized characterization of diseases using sample-specific networks (Nucleic Acids Research, 2016)
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Identifying critical transitions of complex diseases based on a single sample (Bioinformatics, 2014)
- Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers (BMC Medical Genomics, 2013)
- Identifying critical transitions of complex diseases based on a single sample (Bioinformatics, 2014)
- Personalized characterization of diseases using sample-specific networks (Nucleic Acids Research, 2016)