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

Analysis of gene coexpression by B-spline based CoD estimation.

Huai Li1, Yu Sun, Ming Zhan

  • 1Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.

EURASIP Journal on Bioinformatics & Systems Biology
|September 12, 2007
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

Cyclin K condensates bridge CDK12 to phosphorylate and drive oncogenic YAP activation in hepatocellular carcinoma.

Science advances·2026
Same author

MRI radiomics and <sup>90</sup>Y PET dosimetry for predicting hepatocellular carcinoma response after radioembolization.

BMC cancer·2026
Same author

Urinary bisphenol A analogues and metabolically unhealthy obesity in school-age children.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

First large-scale screening of Notch biallelic variants implicates novel candidate genes in congenital hypothyroidism.

The Journal of clinical endocrinology and metabolism·2026
Same author

Removal of pentavalent vanadium from water by Fe-Ni loaded multi-walled carbon nanotubes.

Journal of environmental management·2026
Same author

Editorial: Microbial ecological and biogeochemical processes in the soil-vadose zone-groundwater habitats, volume III.

Frontiers in microbiology·2026
Same journal

Learning directed acyclic graphs from large-scale genomics data.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Bayesian inference for biomarker discovery in proteomics: an analytic solution.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Review of stochastic hybrid systems with applications in biological systems modeling and analysis.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

On biometric systems: electrocardiogram Gaussianity and data synthesis.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Biomedical informatics with optimization and machine learning.

EURASIP journal on bioinformatics & systems biology·2017
See all related articles

A new algorithm, CoexPro, effectively analyzes gene coexpression from microarray data. It uncovers linear and nonlinear relationships, offering insights into gene interactions for network studies.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene coexpression analysis is crucial for understanding gene function and regulatory networks.
  • Existing methods like Pearson's correlation and mutual information have limitations in capturing complex coexpression patterns and directionality.
  • The coefficient of determination (CoD) shows promise but is typically limited to discrete data, risking information loss with continuous microarray data.

Purpose of the Study:

  • To introduce CoexPro, a novel algorithm for comprehensive gene coexpression analysis of continuous microarray data.
  • To address the limitations of existing coexpression indices by developing a method that captures both linear and nonlinear relationships, and suggests directionality.
  • To provide a robust computational tool for gene expression and network studies.

Related Experiment Videos

Main Methods:

  • CoexPro utilizes B-spline approximation to model coexpression patterns between gene pairs.
  • It estimates the coefficient of determination (CoD) from the B-spline approximation for continuous data.
  • The algorithm's performance and utility were validated through simulation studies and functional semantic similarity analysis.

Main Results:

  • CoexPro successfully identifies both linear and a specific class of nonlinear gene coexpression relationships from continuous microarray data.
  • The algorithm provides potential directionality insights for coexpressed genes.
  • Validation studies confirmed the algorithm's effectiveness and reliability.

Conclusions:

  • CoexPro offers a novel and effective approach to gene coexpression analysis, overcoming limitations of previous methods.
  • This algorithm enhances the understanding of gene regulatory networks and facilitates the study of gene interactions.
  • CoexPro is a valuable tool for researchers in genomics and bioinformatics, with demonstrated application in ligand-receptor coexpression analysis.