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

A comparison of physical mapping algorithms based on the maximum likelihood model.

Jinling Huang1, Suchendra M Bhandarkar

  • 1Department of Computer Science, University of Georgia, Athens, Georgia 30602-7404, USA. tupistra@yahoo.com

Bioinformatics (Oxford, England)
|July 23, 2003
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

Klebsormidiophyceae sister genomes reveal multicellularity and stress adaptations.

Journal of phycology·2026
Same author

Preliminary Evaluation of a Large Language Model-Powered Chatbot for Osteoporosis Self-Management Education: Formative Randomized Controlled Trial.

JMIR formative research·2026
Same author

Parameter- and Compute-Efficient Spatial-Spectral Transformer Framework for Pixel-Level Classification of Foreign Plastic Objects on Broiler Meat Using NIR-Hyperspectral Imaging.

Sensors (Basel, Switzerland)·2026
Same author

Association between antioxidant status and special strength performance in combat athletes: a cross-sectional study.

Frontiers in sports and active living·2026
Same author

Genomic divergence, introgression and KASP fingerprinting panel development in five cultivar groups of sweet osmanthus.

BMC plant biology·2026
Same author

Lipocalins regulate development and various stress responses in Physcomitrium patens.

Cell reports·2026
Same journal

Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Bioinformatics (Oxford, England)·2026
Same journal

Quantifying domain-specific relevance of computational biology Wikipedia articles using TF-IDF and cosine similarity.

Bioinformatics (Oxford, England)·2026
Same journal

GATSBI: improving context-aware protein embeddings through biologically motivated data splits.

Bioinformatics (Oxford, England)·2026
Same journal

BiMba: using Vision Mamba to predict protein sites that bind other proteins.

Bioinformatics (Oxford, England)·2026
Same journal

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

Bioinformatics (Oxford, England)·2026
Same journal

Is a Win-Win possible? Achieving pareto-optimal privacy-utility balance in fine-tuned genome language model embeddings against embedding reconstruction attacks.

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

Genetic algorithms (GA) offer superior performance for chromosome physical mapping under the maximum likelihood model, outperforming simulated annealing and large-step Markov chains in efficiency and accuracy.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Physical mapping of chromosomes is computationally complex, requiring discrete and continuous optimization.
  • The maximum likelihood (ML) model is used for physical mapping.
  • Existing discrete optimization methods present challenges in efficiency and solution quality.

Purpose of the Study:

  • To propose and evaluate two genetic algorithm (GA) versions for chromosome physical mapping under the ML model.
  • To compare GA performance against simulated annealing (SA) and large-step Markov chains (LSMC).

Main Methods:

  • Developed two GA versions: heuristic crossover with deterministic replacement, and heuristic crossover with stochastic replacement.
  • Compared GA with SA and LSMC on synthetic and real datasets.

Related Experiment Videos

  • Utilized datasets from cosmid libraries of Neurospora crassa.

    • Main Results:

    • The GA, particularly the stochastic replacement version, consistently outperformed SA and LSMC.
    • GA demonstrated superior runtime efficiency and final solution quality.
    • Experimental results validated the effectiveness of GA on both synthetic and real data.

    Conclusions:

    • Genetic algorithms provide an effective and efficient approach for chromosome physical mapping.
    • The GA with heuristic crossover and stochastic replacement is recommended for physical mapping under the ML model.
    • Further GA improvements for ML-based physical mapping are suggested.