AUTHOR=Liu Rui , Zhong Jiayuan , Yu Xiangtian , Li Yongjun , Chen Pei TITLE=Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model JOURNAL=Frontiers in Genetics VOLUME=10 YEAR=2019 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00285 DOI=10.3389/fgene.2019.00285 ISSN=1664-8021 ABSTRACT=

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.