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

Constructing networks with correlation maximization methods.

Joseph C Mellor1, Jie Wu, Charles Delisi

  • 1Program in Bioinformatics, Boston University. mellor@bu.edu

Genome Informatics. International Conference on Genome Informatics
|February 16, 2005
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

An Agrigenomics Trifecta: Greenhouse Gas Drawdown, Food Security, and New Drugs.

Cold Spring Harbor perspectives in biology·2023
Same author

The Role of Synthetic Biology in Atmospheric Greenhouse Gas Reduction: Prospects and Challenges.

Biodesign research·2023
Same author

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation.

mSystems·2023
Same author

Constructive principles for gene editing oversight.

Nature biotechnology·2022
Same author

Analysis of Covid-19 Data for Eight European Countries and the United Kingdom Using a Simplified SIR Model.

Research square·2020
Same author

Analysis of Covid-19 Data for Eight European Countries and the United Kingdom Using a Simplified SIR Model.

medRxiv : the preprint server for health sciences·2020
Same journal

Linear regression models predicting strength of transcriptional activity of promoters.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Sign: large-scale gene network estimation environment for high performance computing.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Docking-calculation-based method for predicting protein-RNA interactions.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Mechanism of cell cycle disruption by multiple p53 pulses.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Database for crude drugs and Kampo medicine.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

A dynamic programming algorithm to predict synthesis processes of tree-structured compounds with graph grammar.

Genome informatics. International Conference on Genome Informatics·2011
See all related articles

This study introduces methods to extract binary states from gene expression data for systems biology inference. These techniques simplify complex gene regulation networks, enabling robust analysis of biological systems.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Inferring gene regulation and pathway networks is crucial in systems biology.
  • Representing complex biological systems often requires simplifying component states.
  • Discrete 'on'/'off' states offer approximations for gene and protein activity.

Purpose of the Study:

  • To develop techniques for extracting binary state variables from gene expression data.
  • To establish robust measures for statistical significance and information in biological data analysis.
  • To demonstrate the application of these measures in simple gene regulation systems.

Main Methods:

  • Exploration of techniques for extracting binary state variables from gene expression measurements.
  • Development of robust statistical significance and information measures.

Related Experiment Videos

  • Application of measures to simple gene regulatory network models.

    • Main Results:

    • Binary state extraction techniques are presented.
    • Robust measures for statistical significance and information are described.
    • Equivalence of statistical strength and information criteria is shown in limiting cases.

    Conclusions:

    • Binary state representations can approximate complex system behaviors.
    • The developed measures provide robust analysis for gene expression data.
    • This approach aids in understanding gene regulation and pathway networks.