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

Estimating genomic coexpression networks using first-order conditional independence.

Paul M Magwene1, Junhyong Kim

  • 1Department of Biology, University of Pennsylvania, 415 S University Avenue, Philadelphia, PA 19104, USA. paul.magwene@duke.edu

Genome Biology
|December 4, 2004
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

Generation of spermatid-like cells from testicular biopsies obtained from prepubertal boys.

F&S science·2026
Same author

Meiosis-specific genes play roles in ploidy reduction in <i>Cryptococcus neoformans</i> titan cells.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro.

bioRxiv : the preprint server for biology·2026
Same author

Single-cell Transcriptomic Variance Analysis Reveals Intercellular Circadian Desynchrony in the Alzheimer's Affected Human Brain.

bioRxiv : the preprint server for biology·2026
Same author

WeavePop: a bioinformatics workflow to explore and analyze genomic variants of eukaryotic populations.

G3 (Bethesda, Md.)·2026
Same author

Temperature and genetic background drive mobilization of diverse transposable elements in a global human fungal pathogen.

PLoS genetics·2026
Same journal

Somatic mobility of transposons is explosive and shaped by distinct integration biases in Arabidopsis thaliana.

Genome biology·2026
Same journal

UK Biobank whole-genome sequencing reveals robust contributions of rare variants to complex-trait heritability.

Genome biology·2026
Same journal

A one-week automated genome-wide optical pooled screen using OttoSeq.

Genome biology·2026
Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026
Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026
Same journal

Mapping the landscape of allele-specific expression in porcine genomes.

Genome biology·2026
See all related articles

This study introduces an efficient statistical method to build gene coexpression networks. The approach identifies biological pathways and processes within the yeast Saccharomyces cerevisiae.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Understanding gene function requires analyzing relationships between genes.
  • Gene coexpression networks are powerful tools for inferring functional relationships.
  • Existing methods for network inference can be computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient statistical framework for estimating gene coexpression networks.
  • To identify biological pathways and processes using network analysis.
  • To validate the biological relevance of the inferred network.

Main Methods:

  • Utilized first-order conditional independence relationships for association estimation.
  • Applied the framework to microarray gene expression data from Saccharomyces cerevisiae.

Related Experiment Videos

  • Developed and applied an unsupervised graph search algorithm for subgraph discovery.

    • Main Results:

    • Successfully estimated a gene coexpression network for Saccharomyces cerevisiae.
    • Demonstrated that metabolic pathways are coherently represented in the network.
    • Discovered subgraphs corresponding to specific biological processes and gene families.

    Conclusions:

    • The proposed statistical framework is computationally efficient for gene coexpression network inference.
    • The inferred networks provide valuable insights into biological organization and function.
    • The graph search algorithm effectively identifies biologically relevant modules within large networks.