Abstract
Aims/Introduction. 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.
Highlights
A drastic change in the complex biological processes has been shown in recent studies, after which the system shifts rapidly from a stable state to another [1, 2]
To detect the early warning signal of influenza A disease using a small number of samples of high-throughput data, we propose an early warning index serving as a leading indicator to predict the critical transition based on the concept of dynamical network biomarkers proposed by Chen, which drives the disease progression from normal state to disease state
Compared to the general biomarkers [26], dynamical network biomarkers are more suitable for characterizing the transfer of system status
Summary
A drastic change in the complex biological processes has been shown in recent studies, after which the system shifts rapidly from a stable state to another [1, 2]. With further researches on disease progression, as early as 2008, Jin et al applied the protein network to cardiovascular diseases, by identifying a group with high confidence of interacting proteins to form a network, which can be more accurate to divide into two groups of patients compared with a single molecular biomarker [9]. A more important role of network markers and a single molecular biomarker is to distinguish disease status, rather than to detect the critical state of the disease. Given this situation, Chen et al proposed a theory of DNB to identify the critical state of the disease, which was based on model free, small sample, and high-throughput data. Based on individual-specific data, we can predict and identify whether a time period is in predisease state by observing the variation of EWI value combined with the three indicators
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