Diagnosing un-occurred diseases by dynamic network biomarkers @ NIG International symposium 2017

NIG International symposium 2017, Commemorating the 30th Anniversary of DDBJ was held in Mishima Citizens Cultual Hall in shizuoka, Japan. On the third day of the symposium (29 May), oral sessions were held.
In this talk, Luonan Chen makes a presentation entitled "Diagnosing un-occurred diseases by dynamic network biomarkers". (33:24)
All presentations are listed in the YouTube list.
Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions (or un-occurred diseases), even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ gene expression data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data (e.g., liver cancer, lung injury, influenza, type-2 diabetes) and functional analysis. DNB can also be used for the analysis of nonlinear biological processes, e.g., cell differentiation process.
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