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

Using genomics to help predict drug interactions.

Donald Gardner1

  • 1The Rockefeller University, Box 74 (Rizack Lab), 1230 York Avenue, New York, NY 10021, USA. gardner@rockefeller.edu

Journal of Biomedical Informatics
|June 16, 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

A review of pulmonary neutrophilia and insights into the key role of neutrophils in particle-induced pathogenesis in the lung from animal studies of lunar dusts and other poorly soluble dust particles.

Critical reviews in toxicology·2023
Same author

Evaluation of the murine immune response to Xenopsylla cheopis flea saliva and its effect on transmission of Yersinia pestis.

PLoS neglected tropical diseases·2014
Same author

Time to adjuvant therapy and other variables in localized gastric and gastroesophageal junction (GEJ) cancer (IJGC-D-13-00162).

Journal of gastrointestinal cancer·2014
Same author

A Syrian golden hamster model recapitulating ebola hemorrhagic fever.

The Journal of infectious diseases·2012
Same author

Kinetics of innate immune response to Yersinia pestis after intradermal infection in a mouse model.

Infection and immunity·2012
Same author

Pathogenesis and host response in Syrian hamsters following intranasal infection with Andes virus.

PLoS pathogens·2011
Same journal

Causal intervention validation of gene regulatory signals in scGPT.

Journal of biomedical informatics·2026
Same journal

CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms.

Journal of biomedical informatics·2026
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
See all related articles

This study suggests integrating patient genomic data into drug interaction databases to personalize medication recommendations. By using specific genetic information, drug interaction programs can offer more accurate and detailed patient-specific advice.

Area of Science:

  • Pharmacogenomics
  • Bioinformatics
  • Computational Biology

Background:

  • Drug interaction programs currently lack patient-specific genomic data.
  • Cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism.
  • Personalized medicine requires integrating diverse patient data for improved treatment.

Purpose of the Study:

  • To propose a method for incorporating patient genomic information into drug interaction databases.
  • To enhance the accuracy and specificity of drug interaction predictions.
  • To illustrate the application using a specific CYP450 enzyme.

Main Methods:

  • Utilizing Extensible Markup Language (XML) for data formatting.
  • Employing XML tags to integrate patient-specific genomic data.

Related Experiment Videos

  • Combining existing drug interaction information with novel genomic insights.

    • Main Results:

    • Demonstrated a framework for adding genomic data to drug interaction databases.
    • Showcased how XML can structure and store personalized genomic interaction information.
    • Established a pathway for more precise drug interaction profiling.

    Conclusions:

    • Genomic information can significantly improve drug interaction program tailoring.
    • XML-based integration offers a flexible and scalable approach.
    • Personalized pharmacogenomic data enhances the clinical utility of drug interaction software.