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

SemCat: semantically categorized entities for genomics.

Lorraine Tanabe1, Lynne H Thom, Wayne Matten

  • 1National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 24, 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

Exploring Anti-Aging Literature via <i>ConvexTopics</i> and Large Language Models.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Exploring Anti-Aging Literature via <i>ConvexTopics</i> and Large Language Models.

ArXiv·2026
Same author

AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning.

Nature communications·2025
Same author

AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning.

ArXiv·2025
Same author

MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval.

ArXiv·2025
Same author

Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine.

ArXiv·2025
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
See all related articles

We developed SemCat, a semantic database for genomics, to improve natural language processing in biomedical research. This tool aids in classifying biomedical names and recognizing named entities within scientific literature.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Natural language processing (NLP) in the biomedical domain faces challenges due to complex terminology.
  • Existing resources may not adequately capture the semantic nuances of genomic terms.
  • Efficiently processing and understanding large volumes of biomedical text is crucial for research advancement.

Purpose of the Study:

  • To introduce SemCat, a novel semantic database specifically designed for genomics.
  • To demonstrate SemCat's utility in enhancing NLP tasks within the MEDLINE database.
  • To provide a foundation for advanced biomedical text analysis.

Main Methods:

  • Construction of a comprehensive semantic database (SemCat).
  • Inclusion of a large number of semantically categorized names relevant to genomics.

Related Experiment Videos

  • Development of categorization and recognition algorithms.

    • Main Results:

    • SemCat was successfully constructed with extensive semantically categorized genomic names.
    • The database demonstrates effectiveness in facilitating NLP tasks.
    • Successful application in biomedical name classification and named entity recognition.

    Conclusions:

    • SemCat serves as a valuable resource for semantic categorization of genomic terms.
    • The database significantly improves NLP capabilities for biomedical literature analysis.
    • SemCat has practical applications in areas like biomedical name classification and named entity recognition, advancing research in genomics and related fields.