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

What is Evolutionary History?02:35

What is Evolutionary History?

43.7K
Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
43.7K
Evolutionary Psychology01:20

Evolutionary Psychology

1.0K
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
1.0K
Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

377
In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...
377
Parallel Processing01:20

Parallel Processing

742
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...
742
Parallel Resonance01:23

Parallel Resonance

597
The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
597
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

429
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
429

You might also read

Related Articles

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

Sort by
Same author

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2025
Same author

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling.

BioData mining·2025
Same author

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Cell genomics·2025
Same author

Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection.

Patterns (New York, N.Y.)·2025
Same author

AI as an accelerator for defining new problems that transcends boundaries.

BioData mining·2025
Same author

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

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

Related Experiment Video

Updated: Feb 10, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.

Patryk Orzechowski1,2, Moshe Sipper3, Xiuzhen Huang4

  • 1Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.

Bioinformatics (Oxford, England)
|May 24, 2018
PubMed
Summary
This summary is machine-generated.

A new evolutionary computation biclustering algorithm, EBIC, accurately identifies complex gene expression patterns. This AI-driven method is significantly faster and more effective than current state-of-the-art approaches.

More Related Videos

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

3.0K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.5K

Related Experiment Videos

Last Updated: Feb 10, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

3.0K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.5K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence

Background:

  • Biclustering algorithms are crucial for analyzing gene expression data.
  • Accurate identification of biologically relevant patterns remains a significant challenge.
  • Current methods often lack the precision to discover complex biological structures.

Purpose of the Study:

  • Introduce a novel biclustering algorithm, EBIC, for enhanced gene expression data analysis.
  • Develop a method capable of detecting order-preserving patterns with high accuracy.
  • Address limitations of existing biclustering techniques in discovering complex biological relevance.

Main Methods:

  • Developed EBIC, a biclustering algorithm utilizing evolutionary computation (a sub-field of AI).
  • Designed EBIC for parallel processing environments using multiple graphics processing units (GPUs).
  • Evaluated EBIC's performance on synthetic and real gene expression datasets.

Main Results:

  • EBIC demonstrates unprecedented accuracy in discovering multiple complex patterns in gene expression data.
  • The algorithm significantly outperforms state-of-the-art biclustering methods in terms of pattern recovery and relevance.
  • EBIC achieves over 12 times faster results compared to the most accurate reference algorithms.

Conclusions:

  • EBIC offers a highly accurate and efficient solution for biclustering gene expression data.
  • The algorithm's AI foundation and parallel processing capabilities enable superior pattern discovery.
  • EBIC represents a significant advancement in computational biology for identifying biologically relevant structures.