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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
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The hidden Markov model and its applications in bioinformatics analysis.
Yingnan Ma1,2, Haiyan Chen1, Jingxuan Kang1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Genes & Diseases
|October 10, 2025
Summary
Hidden Markov models (HMMs) are powerful statistical tools for analyzing big biological data. This review details HMMs
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Biological big data presents challenges in extracting meaningful insights.
- Hidden Markov models (HMMs) are statistical models effective in machine learning and bioinformatics.
- A comprehensive overview of HMM applications in bioinformatics is needed.
Purpose of the Study:
- To provide a detailed review of Hidden Markov Models (HMMs) in bioinformatics.
- To discuss fundamental HMM concepts, canonical problems, and algorithms.
- To highlight HMM applications and predict future prospects in the field.
Main Methods:
- Overview of HMM fundamental concepts.
- Explanation of the three canonical HMM problems and their resolution algorithms.
- Review of HMM applications in specific bioinformatics domains.
Main Results:
- HMMs are versatile for transmembrane protein prediction, gene discovery, sequence alignment, CpG island detection, and copy number variation analysis.
- The review covers HMM strengths, limitations, and future potential in bioinformatics.
- HMMs offer significant utility in addressing complex biological sequence and system analyses.
Conclusions:
- Hidden Markov Models are pivotal for advancing bioinformatics research.
- This review serves as a comprehensive guide for researchers utilizing HMMs.
- HMMs are essential for understanding complex biological data and systems.
