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

Overview

Journal Front Genet

Date 2019 Apr 26

PMID 31019526

Citations 8

Authors

Rui Liu

Jiayuan Zhong

Xiangtian Yu

Yongjun Li

Pei Chen

Affiliations

Soon will be listed here.

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.

Citing Articles

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