Table_2_Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model.xlsx Rui Liu Jiayuan Zhong Xiangtian Yu Yongjun Li Pei Chen 10.3389/fgene.2019.00285.s003 https://frontiersin.figshare.com/articles/dataset/Table_2_Identifying_Critical_State_of_Complex_Diseases_by_Single-Sample-Based_Hidden_Markov_Model_xlsx/7950071 <p>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.</p> 2019-04-04 04:29:23 hidden Markov process single-sample-based diagnosis dynamical network biomarker (DNB) pre-disease state critical transition early-warning signal