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

Molecular networks in microarray analysis.

Andrey Y Sivachenko1, Anton Yuryev, Nikolai Daraselia

  • 1Ariadne Genomics, Inc., 9430 Key West avenue, Suite 113, Rockville, MD 20850, USA. sivachenko@ariadnegenomics.com

Journal of Bioinformatics and Computational Biology
|July 20, 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

Upregulation of a CFTR mRNA isoform has therapeutic potential for the treatment of 3' CFTR PTC variants.

Molecular therapy. Nucleic acids·2026
Same author

Intra-host GI.1 norovirus evolution is shaped by genetic drift and purifying selection.

Virus evolution·2026
Same author

Unbiased metagenomic exploration of transfusion-transmitted infections with nanopore sequencing.

Transfusion·2025
Same author

GII.17 norovirus re-emerged in the 2020s as a result of dynamic and adaptive evolutionary processes.

Nature communications·2025
Same author

Using machine learning to improve anaphylaxis case identification in medical claims data.

JAMIA open·2024
Same author

GLI1+ perivascular, renal, progenitor cells: The likely source of spontaneous neoplasia that created the AGMK1-9T7 cell line.

PloS one·2023
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
See all related articles

This study presents methods to interpret microarray data by integrating it with molecular network information. These approaches help identify key gene regulators and generate testable biological hypotheses from complex expression datasets.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Microarray data offers genome-wide insights but suffers from noise and lack of structure, hindering interpretation.
  • Integrating expression data with other biological information, like molecular networks, is crucial for biological context.
  • Existing methods for microarray data analysis often lack robust interpretation frameworks.

Purpose of the Study:

  • To develop and demonstrate statistical methods for interpreting microarray data using molecular network information.
  • To identify transcription regulators with significant expression changes by superimposing expression data onto regulatory networks.
  • To compare the performance of different interpretation approaches and assess the biological relevance of predictions.

Main Methods:

Related Experiment Videos

  • Utilizing transcription regulation networks mined from scientific literature.
  • Developing statistical procedures to superimpose gene expression data onto these networks.
  • Implementing tests that consider network topology and the direction of regulatory effects.

Main Results:

  • Demonstrated statistical approaches for analyzing microarray data in the context of molecular networks.
  • Identified transcription regulators exhibiting significant downstream expression patterns.
  • Compared the performance of different methods using two distinct gene expression datasets.

Conclusions:

  • Integrating gene expression data with molecular network information enhances biological interpretation.
  • The proposed statistical methods facilitate the identification of key regulatory elements.
  • These approaches aid in formulating and prioritizing biologically relevant hypotheses from complex genomic data.