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

Compressed representations of macromolecular structures and properties.

Chandrajit Bajaj1, Julio Castrillon-Candas, Vinay Siddavanahalli

  • 1Computational Visualization Center, Department of Computer Sciences and, Institute for Computational and Engineering Sciences, ACES 2.128, 24th & Speedway, University of Texas, Austin, Texas 78712, USA. bajaj@cs.utexas.edu

Structure (London, England : 1993)
|March 16, 2005
PubMed
Summary

We developed a compressed volumetric representation for macromolecular structures and associated properties. This method enhances computational tasks like real-time visualization through fast data access and high compression ratios.

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

The Physics, Information, and Computation of Perennial Learning: Kolmogorov Complexity, Information Distance, and Port-Hamiltonian Thermodynamics.

Entropy (Basel, Switzerland)·2026
Same author

Pathway Anchored Multimodal Clustering Reveals Circuit Level Signatures in Parkinsons Disease.

bioRxiv : the preprint server for biology·2025
Same author

Recipes for when physics fails: recovering robust learning of physics informed neural networks.

Machine learning: science and technology·2023
Same author

Exploring the Study of miR-1301 Inhibiting the Proliferation and Migration of Squamous Cell Carcinoma YD-38 Cells through PI3K/AKT Pathway under Deep Learning Medical Images.

Computational intelligence and neuroscience·2022
Same author

Materials for emergent silicon-integrated optical computing.

Journal of applied physics·2021
Same author

Dynamic Filtering with Large Sampling Field for ConvNets.

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision·2020

Area of Science:

  • Structural biology
  • Computational chemistry
  • Data compression

Background:

  • Macromolecular structures require significant data storage and computational resources.
  • Existing representations can be inefficient for interactive analysis and visualization.
  • Handling associated computed properties alongside structural data presents challenges.

Purpose of the Study:

  • To introduce a unified, compressed volumetric representation for macromolecular structures.
  • To enable efficient handling of associated computed properties at multiple resolutions.
  • To improve performance of computational tasks involving large molecular datasets.

Main Methods:

  • Developed a custom hierarchical wavelet basis for data compression.
  • Implemented a compressed volumetric representation for macromolecular structures.

Related Experiment Videos

  • Focused on achieving fast random data access and decompression.

    • Main Results:

    • The compressed representation offers fast data access and decompression.
    • Achieved high compression ratios for macromolecular structures.
    • Maintained high accuracy for molecular surfaces across multiple resolutions.

    Conclusions:

    • The novel compressed representation significantly enhances computational tasks for large structures.
    • This approach facilitates interactive applications like real-time visualization.
    • The method provides an efficient way to manage macromolecular data and associated properties.