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 Concept Videos

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Statgraphics01:10

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A sleep/wake circuit controls isoflurane sensitivity in Drosophila.

Current biology : CB·2013
Same author

Discordant risk: overweight and cardiometabolic risk in Chinese adults.

Obesity (Silver Spring, Md.)·2013
Same author

Study of the impact of uterine artery embolization (UAE) on endometrial microvessel density (MVD) and angiogenesis.

Cardiovascular and interventional radiology·2013
Same author

Comparative proteomic analysis reveals differentially expressed proteins correlated with fuzz fiber initiation in diploid cotton (Gossypium arboreum L.).

Journal of proteomics·2013
Same author

Sodium intake from various time frames and incident hypertension among Chinese adults.

Epidemiology (Cambridge, Mass.)·2013
Same author

Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, eastern China.

The Science of the total environment·2013
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Fast network centrality analysis using GPUs.

Zhiao Shi1, Bing Zhang

  • 1Advanced Computing Center for Research & Education, Vanderbilt University, Nashville, TN 37203, USA.

BMC Bioinformatics
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces gpu-fan, a software package for fast biological network analysis using GPUs. It significantly speeds up centrality computations for large-scale networks.

Related Experiment Videos

Last Updated: Jun 2, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Network Science

Background:

  • High-throughput technologies generate massive biological data, necessitating powerful computational tools for network analysis.
  • General Purpose computation on Graphics Processing Units (GPGPU) offers a cost-effective solution for large-scale biological network studies.
  • Algorithm design focusing on data parallelism is crucial for maximizing GPU performance.

Purpose of the Study:

  • To develop efficient algorithms for biological network analysis on GPUs.
  • To accelerate shortest path-based centrality computations in large-scale networks.
  • To provide a user-friendly software package for the research community.

Main Methods:

  • Developed an efficient data-parallel formulation for the All-Pairs Shortest Path problem.
  • Created a betweenness centrality algorithm leveraging this formulation and benchmarked it against existing GPU algorithms.
  • Designed algorithms for closeness, eccentricity, and stress centrality based on the core shortest path component.
  • Implemented the gpu-fan software package for CUDA-enabled GPUs.

Main Results:

  • Achieved 11-19% speedup for betweenness centrality on simulated scale-free networks compared to prior GPU methods.
  • Observed 10-50x speedup for centrality computations using gpu-fan on both simulated and real-world biological networks versus CPU implementations.
  • Successfully developed and released the gpu-fan software package.

Conclusions:

  • The gpu-fan package significantly enhances the performance of centrality computations in large-scale biological networks.
  • This tool empowers researchers to analyze complex biological networks more efficiently.
  • Source code for gpu-fan is publicly available under the GNU General Public License (GPL).