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

semCDI: a query formulation for semantic data integration in caBIG.

E Patrick Shironoshita1, Yves R Jean-Mary, Ray M Bradley

  • 1INFOTECH Soft, Inc., 9200 Dadeland Blvd., Ste 620, Miami, FL 33156, USA.

Journal of the American Medical Informatics Association : JAMIA
|April 26, 2008
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

MARML: Motif-Aware Deep Representation Learning in Multilayer Networks.

IEEE transactions on neural networks and learning systems·2023
Same author

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.

IEEE/ACM transactions on computational biology and bioinformatics·2023
Same author

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics·2020
Same author

Ontology-based metabolomics data integration with quality control.

Bioanalysis·2019
Same author

Ontology Matching with Semantic Verification.

Web semantics (Online)·2010
Same author

semQA: SPARQL with Idempotent Disjunction.

IEEE transactions on knowledge and data engineering·2009
Same journal

Digital divide in clinical and operational artificial intelligence adoption and implementation stages: US hospital diffusion patterns and AI deserts.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Extending the fundamental theorem of biomedical informatics: a proposal and illustrative examples.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Human factors methods for designing safe health information technology: what do the experts think?

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Equity-by-design for socially assistive robots as digital health tools.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Orchestrator multi-agent clinical decision support system for secondary headache diagnosis in primary care.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

CUI-Curate: a GraphRAG-based framework for automated clinical concept curation for NLP applications.

Journal of the American Medical Informatics Association : JAMIA·2026
See all related articles

This study introduces semCDI, a novel query formulation for cancer data services within the cancer Biomedical Informatics Grid (caBIG). It enables efficient data integration and exploratory searching of cancer-related information.

Area of Science:

  • Biomedical Informatics
  • Cancer Research Data Management

Background:

  • The cancer Biomedical Informatics Grid (caBIG) offers a wealth of cancer-related data services.
  • Effective querying and integration of these distributed data sources remain a challenge.

Purpose of the Study:

  • To develop a query formulation mechanism for the semantic representation of caBIG data services.
  • To establish a foundation for a semantic data integration system for cancer research.

Main Methods:

  • Utilized an ontology view of caBIG semantic concepts, metadata, and data.
  • Employed the SPARQL query language, extended with Horn rules, for query specification.
  • Enabled data joining through object properties and data merging via Common Data Elements (CDEs) using Horn rules.

Related Experiment Videos

Main Results:

  • Developed the semCDI query formulation for semantic data integration.
  • Validated the formulation through a prototype and execution of two queries against caBIG data services.

Conclusions:

  • semCDI leverages caBIG's semantic metadata for robust query building and data integration.
  • This formulation enhances efficient querying and exploratory searching of cancer data, with future potential as caBIG expands.