Abstract
BackgroundDuring the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect.ResultsAbnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt.ConclusionsThese results all indicate that the static model in pathways could simplify the detection of the early-warning signals.
Highlights
During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors
We applied sample signal perturbation (SSP) to three datasets. our results show that SSP outperforms sample landscape entropy (SLE) in predicting the early-warning signals
Complex diseases arise is due to the accumulation of differential expressions and signal perturbations in a subgroup of genes allowing uncontrolled biological functions
Summary
During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. Theoretical considerations and computational studies suggest that many types of complex dynamical systems may have a critical points between an ordered and a disordered dynamical regime [1, 2] This regime provides complex systems to have an optimal balance between robustness and adaptability. Bifurcation theory demonstrates that complex systems may undergo a sudden state transition under some critical continuous perturbations of various internal or externals. Such a change often occurs at a critical threshold, or the so-called ‘‘tipping point’’, at which the system shifts abruptly from one state to another
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