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

Area of Science:

• Computational Biology
• Systems Biology
• Biostatistics

Background:

• Complex diseases progress through normal, pre-disease, and disease states.
• Identifying the pre-disease state is crucial for early intervention but challenging due to subtle differences.
• Current methods struggle with single-sample analysis for detecting early disease transitions.

Purpose of the Study:

• To develop an individual-specific method for identifying the pre-disease state.
• To signal critical transitions in complex diseases before irreversible deterioration.
• To analyze dynamical differences between normal and pre-disease states using a single-sample approach.

Main Methods:

• A single-sample-based hidden Markov model (HMM) was proposed.
• The tipping point was modeled as the endpoint of a stationary Markov process.
• The HMM approach was used to explore dynamical differences between normal and pre-disease states.

Main Results:

• The proposed HMM method successfully identified pre-disease states in numerical simulations.
• Effectiveness was demonstrated in real-world datasets, including stomach adenocarcinoma.
• The method also showed efficacy in analyzing influenza infection dynamics.

Conclusions:

• The single-sample HMM is effective for detecting pre-disease states and critical transitions.
• This approach offers a promising tool for early detection of complex diseases.
• The method facilitates individual-specific disease progression monitoring.