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

dbZach toxicogenomic information management system.

Lyle D Burgoon1, Timothy R Zacharewski

  • 1Michigan State University, Toxicogenomic Informatics and Solutions, LLC and Department of Biochemistry and Molecular Biology, National Food Safety and Toxicology Center, Center for Integrative Toxicology, MI, USA. burgoonL@msu.edu

Pharmacogenomics
|February 28, 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

Challenges and Opportunities in the Development and Adoption of New Approach Methods (NAMs).

Journal of agricultural and food chemistry·2025
Same author

Octamethylcyclotetrasiloxane (D4) lacks endocrine disruptive potential via estrogen pathways.

Archives of toxicology·2025
Same author

Kinetically-derived maximal dose (KMD) confirms lack of human relevance for high-dose effects of octamethylcyclotetrasiloxane (D4).

Archives of toxicology·2025
Same author

The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints.

Regulatory toxicology and pharmacology : RTP·2024
Same author

Range of the perfluorooctanoate (PFOA) safe dose for human health: An international collaboration.

Regulatory toxicology and pharmacology : RTP·2024
Same author

The physiological and biochemical basis of potency thresholds modeled using human estrogen receptor alpha: implications for identifying endocrine disruptors.

Archives of toxicology·2024
Same journal

Closely similar tacrolimus morning troughs and graft function across the recipients' common <i>ABCB1</i> polymorphisms (<i>c.2677G>T/A</i>, <i>c.1236C>T</i>, <i>c.3435C>T</i>) early after renal transplantation.

Pharmacogenomics·2026
Same journal

Pharmacogenomics for stratified antidepressant treatment in major depressive disorder: evidence, limits and a roadmap for clinical use.

Pharmacogenomics·2026
Same journal

Guidance from an informed public on collecting and sharing PGx test results for major depressive disorder: "It's no different from your blood type".

Pharmacogenomics·2026
Same journal

Optimizing pharmacogenomics workflows: a qualitative evaluation of clinical service delivery models.

Pharmacogenomics·2026
Same journal

Combinations of single gene predictors with AI-driven polygenic precision therapies in cardiovascular diseases.

Pharmacogenomics·2026
Same journal

Practical guide to implementing pre-emptive pharmacogenetic screening in routine pediatric oncology care.

Pharmacogenomics·2026
See all related articles

Quantitative risk assessment needs integrated biological data. The dbZach toxicogenomic information management system facilitates data sharing and analysis for improved toxicity prediction and biomarker discovery.

Area of Science:

  • Computational toxicology and bioinformatics
  • Data management and integration in biological sciences

Background:

  • Quantitative risk assessment and toxicity mechanism elucidation demand advanced computational tools.
  • Integrating diverse biological data across the source-to-outcome continuum is crucial for accurate assessments.
  • Existing data management solutions are essential for handling disparate toxicological and toxicogenomic information.

Purpose of the Study:

  • To introduce the dbZach toxicogenomic information management system.
  • To highlight the system's capability in integrating traditional toxicology and toxicogenomic data.
  • To facilitate comprehensive data integration, analysis, and sharing for improved toxicity prediction.

Main Methods:

  • Development of a modular relational database (dbZach) with data insertion, retrieval, and mining tools.

Related Experiment Videos

  • Integration of traditional toxicology data with complementary toxicogenomic data.
  • Establishment of a community dialog to define data reporting and exchange standards for toxicogenomics.

    • Main Results:

    • dbZach enables the management and integration of disparate data for the source-to-outcome continuum.
    • Software solutions can be created to identify predictive biomarkers for exposures and molecular responses.
    • Facilitation of historical assessment evaluation and cross-technology, species, and chemical class data comparison.

    Conclusions:

    • Enterprise data management solutions, like dbZach, are vital for advancing quantitative risk assessment.
    • The system supports the development of predictive biomarkers and enhances data analysis capabilities.
    • Widespread toxicogenomic data sharing requires community collaboration and standardized reporting structures.