Periodic Lyapunov differential equation for noise evaluation in oscillatory genetic networks

Ichikawa, Sho; Itô, Yôhei; Uchida, Kenko

doi:10.1109/cca.2009.5281071

Cited by 2 publications

(1 citation statement)

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“…In this approach, the traditional node-network representing the first-order statistics distinguishes the disease from the normal state, whereas the edge-network representing the higher-order statistics (ENA: edge-network analysis) distinguishes the pre-disease state from the normal state. At the molecular level, a biological system can be described by a stochastic dynamics model comprising a master equation or stochastic differential equations [52]. By linearizing the system and assuming a Gaussian distribution of the components, the system can be exactly expressed by two sets of equations; one evolving the mean vector of molecules (first-order statistical representation), and the other evolving the covariance matrix of molecules (second-order statistical representation).…”

Section: Dynamical Network Biomarkers and Dynamical Edge Biomarkersmentioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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Section: Dynamical Network Biomarkers and Dynamical Edge Biomarkersmentioning

confidence: 99%