Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers

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 dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or 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 microarray 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 and functional analysis

Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model

The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method

Overview of Network Analysis in Systems Medicine

Systems Medicine (SM) is an interdisciplinary research paradigm, that heavily relieson complex systems theory, and emphasizes on the studies the human body in termsof systems and the interactions among them, incorporating biochemical,physiological, and environment interactions. The article presents developments in SMresearch, focusing specifically on the network analysis approaches. Network analysisis fundamental for the study of interactions among systems at different levels withinthe human body. The background knowledge is established: the basic concepts ofnodes and edges, and network metrics as well as existing computational tools aredescribed. Different applications in health research are discussed, includingdescriptive and predictive approaches. The use of network analysis in temporal dataand data coming from digital health technologies is further highlighted. Finally, thecurrent challenges are discussed and the foreseen development