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

Weighting hidden Markov models for maximum discrimination

R Karchin1, R Hughey

  • 1Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. rph@cse.ucsc.edu

Bioinformatics (Oxford, England)
|January 27, 1999
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

Corrigendum to "HLA class II immunogenic mutation burden predicts response to immune checkpoint blockade": [Annals of Oncology volume 33 (2022) 728-738].

Annals of oncology : official journal of the European Society for Medical Oncology·2023
Same author

HLA class II immunogenic mutation burden predicts response to immune checkpoint blockade.

Annals of oncology : official journal of the European Society for Medical Oncology·2022
Same author

Genomic alterations in head and neck squamous cell carcinoma determined by cancer gene-targeted sequencing.

Annals of oncology : official journal of the European Society for Medical Oncology·2015
Same author

Sequence analysis of 515 kinase genes in chronic lymphocytic leukemia.

Leukemia·2011
Same author

Identification of aberrant pathway and network activity from high-throughput data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2010
Same author

Energy function-based approaches to graph coloring.

IEEE transactions on neural networks·2008
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

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

This study introduces sequence weighting for Hidden Markov Models (HMMs) to improve generalization. Weighting methods significantly reduce errors, enhancing model accuracy for biological sequence analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Hidden Markov Models (HMMs) are effective for modeling biological sequences.
  • Biased training sets can lead to insufficiently general HMMs.
  • Sequence weighting methods based on maximum discrimination are evaluated.

Purpose of the Study:

  • To evaluate sequence weighting methods for improving HMM generalization.
  • To address the issue of biased training sets in HMMs.
  • To enhance the accuracy of statistical representations of biological sequences.

Main Methods:

  • Implementing sequence weighting using an exponential scaling factor (0.1 to 1.0).
  • Training HMMs with varying numbers of sequences (1, 2, 5, 10).
  • Comparing performance against Probabilistic Smith-Waterman, HMMer, and Meta-MEME.

Related Experiment Videos

Main Results:

  • Training with 5 or 10 sequences reduced errors by 20% and 51%, respectively.
  • The enhanced SAM HMM suite outperformed HMMer (17% error reduction) and Meta-MEME (28% error reduction).
  • Unweighted SAM showed a 31% error increase compared to the weighted version.

Conclusions:

  • Sequence weighting significantly improves HMM generalization and accuracy.
  • The proposed method offers a substantial improvement over existing tools for sequence analysis.
  • The SAM HMM suite with weighting provides a more robust model for biological sequence data.