Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

HMMGEP: clustering gene expression data using hidden Markov models.

Xinglai Ji1, Yuan Yuan, Jesse Li-Ling

  • 1Institute of Bioinformatics, Tsinghua University, Beijing 100084, Peoples Republic of China.

Bioinformatics (Oxford, England)
|February 28, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Creep Characteristics and Damage Constitutive Model of White Sandstone Under Short-Term Freeze-Thaw Cycles.

Materials (Basel, Switzerland)·2026
Same author

Bio-inspired programmable assembly of shape-memory and blood-reinforced cryogel for hemostasis and functional liver regeneration.

Materials today. Bio·2026
Same author

Comparison of the outcomes of <i>in vitro</i> fertilization and embryo transfer among ethnic Chinese Yi and Han women: a multicenter retrospective cohort study.

PeerJ·2026
Same author

Zwitterionic peptide dendrimer-based nanocarriers with enhanced stability and biocompatibility for cancer therapy.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Genome-Wide Identification of WRKY Group II Genes and the Role of <i>HbWRKY11</i> in <i>Hordeum brevisubulatum</i> Under Saline-Alkali Stress.

Plants (Basel, Switzerland)·2026
Same author

Impact of inactivated SARS-CoV-2 vaccination on blastocyst euploidy status: a prospective cohort study of patients undergoing preimplantation genetic testing for aneuploidy in China.

BMC pregnancy and childbirth·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

The HMMGEP package offers cluster analysis for gene expression data. It utilizes hidden Markov models for robust biological data interpretation and discovery.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis is crucial for understanding biological processes.
  • Hidden Markov Models (HMMs) provide a powerful framework for sequence and pattern recognition in biological data.
  • Existing methods may have limitations in handling the complexity of gene expression profiles.

Purpose of the Study:

  • To introduce HMMGEP, a novel software package for gene expression data analysis.
  • To leverage hidden Markov models for enhanced cluster analysis of gene expression data.
  • To provide researchers with a user-friendly tool for exploring gene expression patterns.

Main Methods:

  • HMMGEP employs hidden Markov models (HMMs) for probabilistic modeling of gene expression patterns.

Related Experiment Videos

  • The package implements advanced algorithms for unsupervised cluster analysis.
  • It integrates data preprocessing and visualization tools for comprehensive analysis.

    • Main Results:

    • HMMGEP enables effective clustering of gene expression profiles based on HMMs.
    • The package demonstrates robust performance in identifying biologically relevant gene clusters.
    • It facilitates the discovery of novel gene expression signatures.

    Conclusions:

    • HMMGEP is a valuable tool for performing cluster analysis on gene expression data.
    • The use of hidden Markov models enhances the accuracy and interpretability of gene expression clustering.
    • The package is readily available with source code and documentation for the research community.