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

Hidden Markov model variants and their application.

Stephen Winters-Hilt1

  • 1Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA. winters@cs.uno.edu

BMC Bioinformatics
|November 23, 2006
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

Nanopore Event-Transduction Signal Stabilization for Wide pH Range under Extreme Chaotrope Conditions.

Molecules (Basel, Switzerland)·2016
Same author

Proceedings of the 2011 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference. Introduction.

BMC bioinformatics·2011
Same author

The NTD Nanoscope: potential applications and implementations.

BMC bioinformatics·2011
Same author

Pattern recognition-informed feedback for nanopore detector cheminformatics.

Advances in experimental medicine and biology·2010
Same author

Proceedings of the 2008 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.

BMC bioinformatics·2008
Same author

Hybrid MM/SVM structural sensors for stochastic sequential data.

BMC bioinformatics·2008
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Markov statistical methods enable unsupervised learning for identifying prokaryotic genomic structures. This approach, utilizing hidden Markov models (HMMs), also enhances eukaryotic gene prediction by addressing alternative splicing complexities.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Unsupervised learning offers potential for automated genomic structure identification.
  • Hidden Markov Models (HMMs) are widely used but face limitations in complex genomic regions.
  • Comparative genomics requires robust methods for analyzing prokaryotic and eukaryotic genomes.

Purpose of the Study:

  • To develop an unsupervised learning process for comprehensive genomic structure identification in prokaryotes.
  • To improve eukaryotic gene prediction by addressing limitations in HMMs, particularly with alternative splicing.
  • To introduce novel statistical methods for gene-finding and genomic analysis.

Main Methods:

  • Application of Markov statistical methods, including mutual information and probabilistic measures.

Related Experiment Videos

  • Development of a multi-pass, 'bootstrap' learning process for iterative refinement.
  • Expansion of HMM states for two-layer DNA parsing in eukaryotic gene prediction.
  • Utilization of a novel hash-interpolating Markov model (hIMM) and HMM-with-Duration.

    • Main Results:

    • Demonstrated potential for unsupervised identification of prokaryotic genomic structures.
    • Showcased an improved HMM approach for eukaryotic gene prediction, handling alternative splicing.
    • Introduced new statistical tools applicable to gene structure analysis and other biological data.

    Conclusions:

    • Markov statistical methods provide a powerful framework for unsupervised genomic analysis in prokaryotes.
    • Enhanced HMM architectures can overcome limitations in eukaryotic gene prediction, especially for alternatively spliced genes.
    • The developed methods offer a common ground for comparative genomics and serve as valuable knowledge discovery tools.