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Related Concept Videos

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Updated: Jan 10, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using

Claudia Giuliani1, Gita Benadi1, Felix Engel1

  • 1Institute of Medical Biometry and Statistics, Medical Faculty and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, Freiburg, 79104, Germany, 49 076127083739.

JMIR Formative Research
|November 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 4-step approach using GPT-4o and PubTator 3.0 for automated biomedical entity extraction and dataset metadata annotation. The method achieved 98% annotation precision, demonstrating its potential for routine biomedical metadata generation.

Keywords:
AICAGGPT-4oPubTator 3.0artificial intelligencebiomedical entitiescache-augmented generationmetadata annotation

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Data Science

Background:

  • Accurate extraction of biomedical entities is crucial for effective metadata annotation of research datasets.
  • This ensures data findability, accessibility, interoperability, and reusability in collaborative research.

Purpose of the Study:

  • To introduce a novel 4-step cache-augmented generation approach for automated biomedical entity identification.
  • Leveraging GPT-4o and PubTator 3.0 for dataset metadata annotation.

Main Methods:

  • A 4-step process: GPT-4o for candidate entity generation, PubTator 3.0 for validation, schema-based term extraction, and combined evaluation.
  • Applied to 23 articles from the Collaborative Research Center OncoEscape, validated via author interviews and random-effects meta-analysis.

Main Results:

  • Generated a mean of 19.6 schema-related and 6.7 PubTator-validated biomedical entities per article.
  • Achieved an overall annotation precision of 98% (95% CI 94%-100%), with errors mainly in non-basic research articles.
  • Including supplemental material did not improve precision (98%, 95% CI 95%-100%).

Conclusions:

  • Large language models show significant potential for supporting metadata annotation workflows.
  • The findings support the practical feasibility of full-text analysis for routine biomedical metadata generation.