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

Finding subtle motifs with variable gaps in unaligned DNA sequences.

Yuh-Jyh Hu1

  • 1Computer and Information Science Department, National Chiao-Tung University, 1001 Ta Shueh Road, Hsinchu, Taiwan, ROC. yhu@cis.nctu.edu.tw

Computer Methods and Programs in Biomedicine
|December 7, 2002
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

Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores.

Mycoses·2023
Same author

Periprosthetic Joint Infection Prediction via Machine Learning: Comprehensible Personalized Decision Support for Diagnosis.

The Journal of arthroplasty·2021
Same author

Residue-Residue Interaction Prediction via Stacked Meta-Learning.

International journal of molecular sciences·2021
Same author

Application of Meta Learning to B-Cell Conformational Epitope Prediction.

Methods in molecular biology (Clifton, N.J.)·2020
Same author

Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme.

BMC bioinformatics·2019
Same author

Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.

IEEE journal of biomedical and health informatics·2017
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026
Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026
Same journal

Spectral super-resolution for Parkinson's voice via representation-level methods under mixed-reality acquisition.

Computer methods and programs in biomedicine·2026
See all related articles

This study introduces a novel iterative-restart algorithm for identifying gene regulatory elements. The new method effectively detects complex combinatorial signals with variable gaps, outperforming existing motif-finding algorithms.

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Gene expression regulation relies on short DNA sequences near genes.
  • Identifying these regulatory sequences is crucial but challenging.
  • Existing motif-finding algorithms struggle with complex biological signals.

Purpose of the Study:

  • To develop a new algorithm for detecting short regulatory DNA sequences (motifs).
  • To address the challenge of finding combinatorial motifs with variable gaps.
  • To improve upon existing motif detection methods.

Main Methods:

  • An iterative-restart design was employed for motif detection.
  • The algorithm was extended to handle combinatorial signals with variable gaps.
  • Performance was evaluated on challenge problems and real biological data.

Related Experiment Videos

Main Results:

  • The new algorithm successfully identified target motifs in the Challenge Problem.
  • It demonstrated effectiveness in finding combinatorial signals with variable lengths.
  • The algorithm showed superior performance compared to representative existing methods.

Conclusions:

  • The iterative-restart algorithm provides an effective solution for motif detection.
  • The extended approach successfully addresses complex regulatory signal identification.
  • This work advances computational methods for understanding gene regulation.