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

Parallel computing in biomedical research

R L Martino1, C A Johnson, E B Suh

  • 1Computational Bioscience and Engineering Laboratory, National Institutes of Health, Bethesda, MD 20892.

Science (New York, N.Y.)
|August 12, 1994
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

Spectral signal-to-noise ratio and resolution assessment of 3D reconstructions.

Journal of structural biology·2005
Same author

Capsid structure of Kaposi's sarcoma-associated herpesvirus, a gammaherpesvirus, compared to those of an alphaherpesvirus, herpes simplex virus type 1, and a betaherpesvirus, cytomegalovirus.

Journal of virology·2001
Same author

Molecular architecture of bacteriophage T4 capsid: vertex structure and bimodal binding of the stabilizing accessory protein, Soc.

Virology·2000
Same author

Isolation of herpes simplex virus procapsids from cells infected with a protease-deficient mutant virus.

Journal of virology·2000
Same author

Molecular tectonic model of virus structural transitions: the putative cell entry states of poliovirus.

Journal of virology·2000
Same author

Three-dimensional structure of poliovirus receptor bound to poliovirus.

Proceedings of the National Academy of Sciences of the United States of America·2000
Same journal

Erratum for the Research Article "Detecting supramolecular organic nanoparticles during heat wave".

Science (New York, N.Y.)·2026
Same journal

Local signals, systemic decline.

Science (New York, N.Y.)·2026
Same journal

The mechanics of liver regeneration.

Science (New York, N.Y.)·2026
Same journal

Computing in a memory with physics.

Science (New York, N.Y.)·2026
Same journal

Retraction.

Science (New York, N.Y.)·2026
Same journal

Making time.

Science (New York, N.Y.)·2026
See all related articles

Scalable parallel computing accelerates biomedical research by enabling advanced algorithms for biological structure and function analysis. These methods significantly outperform traditional systems in tasks like protein structure prediction and sequence searching.

Area of Science:

  • Biomedical Computing
  • Computational Biology
  • Bioinformatics

Background:

  • Advanced biomedical research requires significant computational power.
  • Scalable parallel computer architectures offer a solution for complex computational problems.
  • The National Institutes of Health (NIH) has focused on developing parallel algorithms for biological applications.

Purpose of the Study:

  • To present NIH-developed parallel algorithms and techniques for biological structure and function determination.
  • To showcase the application of these algorithms in areas such as electron microscopy, protein structure prediction, and sequence database searching.
  • To demonstrate the performance benefits of parallel computing in biomedical applications.

Main Methods:

  • Development and implementation of parallel algorithms for specific biomedical tasks.

Related Experiment Videos

  • Processing of electron micrograph data for viral three-dimensional structure determination.
  • Calculation of solvent-accessible surface area for protein conformation prediction.
  • Searching large biological databases for homologous DNA and amino acid sequences using parallel techniques.

    • Main Results:

    • Demonstrated substantial performance improvements using parallel implementations.
    • Achieved significant speedups compared to conventional sequential computing systems.
    • Validated the effectiveness of parallel algorithms across diverse biomedical computing problems.

    Conclusions:

    • Scalable parallel computing architectures are crucial for addressing demanding biomedical computational challenges.
    • NIH's parallel algorithms provide significant performance gains for determining biological structure and function.
    • Parallel computing offers a powerful approach for accelerating discovery in bioinformatics and computational biology.