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

Genetic network inference: from co-expression clustering to reverse engineering.

P D'haeseleer1, S Liang, R Somogyi

  • 1University of New Mexico, Department of Computer Science, Albuquerque, NM 87131, USA. patrik@cs.umm.edu

Bioinformatics (Oxford, England)
|December 1, 2000
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

[Efficacy and safety of transvaginal assisted total intracavitary orthotopic neobladder surgery for female urothelial carcinoma of the bladder].

Zhonghua yi xue za zhi·2026
Same author

Challenging Spontaneous Quantum Collapse with the XENONnT Dark Matter Detector.

Physical review letters·2026
Same author

Evidence for the Collective Nature of Radial Flow in Pb+Pb Collisions with the ATLAS Detector.

Physical review letters·2026
Same author

[Research progress on risk factors and risk prediction of cardiovascular diseases related to antiphospholipid syndrome].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2026
Same author

Evidence for the Dimuon Decay of the Higgs Boson in pp Collisions with the ATLAS Detector.

Physical review letters·2025
Same author

WIMP Dark Matter Search Using a 3.1 Tonne-Year Exposure of the XENONnT Experiment.

Physical review letters·2025
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

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

This review explores data mining and modeling techniques to understand gene regulatory networks from high-throughput gene expression data. Advanced methods like clustering and network reverse engineering are key to deciphering gene interactions and biological functions.

Area of Science:

  • Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Modern biological research utilizes advanced molecular, analytical, and computational technologies to investigate complex biological systems.
  • High-throughput gene expression assays provide crucial data on gene regulatory network outputs.

Purpose of the Study:

  • To review datamining and modeling approaches for analyzing gene expression datasets.
  • To conceptualize and unravel functional relationships within biological systems.
  • To explore methods for inferring causal gene interactions.

Main Methods:

  • Clustering of co-expression profiles to infer shared regulatory inputs and functional pathways.
  • Discussion of clustering aspects: distance measures, algorithms, and multiple-cluster memberships.

Related Experiment Videos

  • Exploration of reverse engineering approaches for genetic networks, including Boolean, linear, and non-linear models.

    • Main Results:

    • Co-expression clustering aids in identifying genes with shared regulatory mechanisms and biological pathways.
    • Various computational models, from discrete to continuous, are available for reverse engineering genetic networks.
    • Inferring direct causal connections between genes is a key goal of advanced network analysis.

    Conclusions:

    • Combining predictive modeling with experimental validation is essential for a deeper understanding of biological systems.
    • These approaches are vital for advancing therapeutic targeting and bioengineering applications.
    • Systematic investigation of gene regulatory networks offers insights into living organisms.