Title: Hunt for disease tipping points by dynamic network biomarkers
Speaker: Luonan Chen, Shanghai Institutes for Biological Sciences, CAS.
Abstract: 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, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamic network biomarker (DNB) that serves as a general early-warning signal indicating a 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 expression data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by the related experimental data and functional analysis.
