Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Large Language Model Ensemble for Automated TNM Staging from Radiology Reports.

Bioinformatics (Oxford, England)·2026

Same author

Emotion Classification in Japanese Cancer Survivor Interview Narratives Using Sentiment Polarity and Plutchik Emotion Frameworks: Model Development and Evaluation Study.

JMIR formative research·2026

Same author

Correction: Angiography- and IVUS-tip-detection-guided 3D wiring for CTO and non-CTO percutaneous coronary interventions.

Cardiovascular intervention and therapeutics·2026

Same author

Small Molecules as Advanced Therapy for the Treatment of Inflammatory Bowel Disease, Particularly Regarding Ulcerative Colitis.

Internal medicine (Tokyo, Japan)·2026

Same author

A Multiassessment and Multiprofessional Agents Approach for Medical Chatbot Risk Estimation: Development and Evaluation Study.

JMIR medical informatics·2026

Same author

Angiography- and IVUS-tip-detection-guided 3D wiring for CTO and non-CTO percutaneous coronary interventions.

Cardiovascular intervention and therapeutics·2026

Search research articles

Related Experiment Video

Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Efficient Maintenance of Large-Scale Medical Dictionaries Using Large Language Models: A Case for Biomarkers.

Yuka Otsuki¹, Shuntaro Yada^1,2, Tomohiro Nishiyama¹

¹Nara Institute of Science and Technology, Japan.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This study introduces an automated method using large language models to correct medical dictionary metadata, significantly reducing maintenance costs and effort for large-scale biomedical resources.

Keywords:

Biomarker Data resource Human-in-the-loop Large Language Models Natural Language Processing Ontology

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Natural Language Processing
Bioinformatics
Medical Informatics

Background:

Dictionaries are crucial for Natural Language Processing (NLP) tasks but are costly to build and maintain.
Manual updates for large-scale dictionaries are time-consuming and expensive.
Leveraging revision histories can automate corrections for unedited terms, improving quality and reducing costs.

Purpose of the Study:

To propose and evaluate a method for automatically correcting metadata in a large-scale medical dictionary.
To reduce the burden of dictionary maintenance using automated techniques.
To assess the efficacy of large language models (LLMs) in zero-shot settings for this task.

Main Methods:

Utilized large language models (LLMs) capable of zero-shot learning to estimate dictionary information.
Applied the method to a medical dictionary with over 500,000 terms.
Conducted experiments focused on gene biomarker expression variations, requiring specialized medical knowledge.

Main Results:

The proposed method demonstrated effective automatic correction of metadata in a large-scale medical dictionary.
LLMs provided dictionary information without requiring task-specific configurations.
Experiments showed a significant reduction in the dictionary maintenance burden.

Conclusions:

Automated metadata correction using LLMs is a viable and cost-effective solution for large-scale medical dictionaries.
This approach enhances dictionary quality while minimizing manual effort.
The zero-shot capabilities of LLMs are well-suited for specialized biomedical tasks like dictionary enrichment.