Evidence map›Paper›PMID 41069576›Full record

ReviewGenes & diseases2026

The hidden Markov model and its applications in bioinformatics analysis.

Yingnan Ma, Haiyan Chen, Jingxuan Kang, Xuying Guo, Chen Sun, Jing Xu, Junxian Tao, Siyu Wei, Yu Dong, Hongsheng Tian and 8 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genes & diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Yingnan MaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Haiyan ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Jingxuan KangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Xuying GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Chen SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Jing XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Junxian TaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Siyu WeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Yu DongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Hongsheng TianCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Wenhua LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Zhe JiaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Shuo BiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Zhenwei ShangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Chen ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Hongchao LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Yongshuai JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
Mingming ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Big biological data contains a large amount of life science information, yet extracting meaningful insights from this data remains a complex challenge. The hidden Markov model (HMM), a statistical model widely utilized in machine learning, has proven effective in addressing various problems in bioinformatics. Despite its broad applicability, a more detailed and comprehensive discussion is needed regarding the specific ways in which HMMs are employed in this field. This review provides an overview of the HMM, including its fundamental concepts, the three canonical problems associated with it, and the relevant algorithms used for their resolution. The discussion emphasizes the model's significant applications in bioinformatics, particularly in areas such as transmembrane protein prediction, gene discovery, sequence alignment, CpG island detection, and copy number variation analysis. Finally, the strengths and limitations of the HMM are discussed, and its prospects in bioinformatics are predicted. HMMs can play a pivotal role in addressing complex biological problems and advancing our understanding of biological sequences and systems. This review can provide bioinformatics researchers with comprehensive information on HMM and guide their work.

Indexed as

Copy number variation detectionCpG island predictionGene findingHidden Markov modelsSequence alignmentTransmembrane protein prediction

Identifiers

PMID41069576
PMCPMC12505677

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.