Detecting Early Warning Signal of Influenza A Disease Using Sample-Specific Dynamical Network Biomarkers.

<italic>Aims/Introduction</italic>. Evidences have shown that the deteriorated procession of disease is not a smooth change with time and conditions, in which a critical transition point denoted as predisease state drives the state from normal to disease. Considering individual differences, this pap...

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Bibliographic Details
Published in:BioMed Research International Vol. 2018; pp. 1 - 8
Main Authors: Zhu, Shanshan, Gao, Jie, Ding, Tao, Xu, Junhua, Wu, Min
Format: equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 1/31/2018
Online Access:View this record in EBSCOhost
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Summary:<italic>Aims/Introduction</italic>. Evidences have shown that the deteriorated procession of disease is not a smooth change with time and conditions, in which a critical transition point denoted as predisease state drives the state from normal to disease. Considering individual differences, this paper provides a sample-specific method that constructs an index with individual-specific dynamical network biomarkers (DNB) which are defined as early warning index (EWI) for detecting predisease state of individual sample. Based on microarray data of influenza A disease, 144 genes are selected as DNB and the 7th time period is defined as predisease state. In addition, according to functional analysis of the discovered DNB, it is relevant with experience data, which can illustrate the effectiveness of our sample-specific method.