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Improving Translational Accuracy02:07

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...
Improving Translational Accuracy02:07

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...

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Related Experiment Video

Updated: May 29, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model

Elaheh Aghaarabi1, David Murray1

  • 1Office of Disease Prevention, National Institutes of Health, 6705 Rockledge Dr, Bethesda, MD, 20892, United States, 1 3014964000.

JMIR Medical Informatics
|May 9, 2025
PubMed
Summary
This summary is machine-generated.

A new AI model accurately identifies complex clinical trial designs, including group- or cluster-randomized trials (GRTs) and stepped wedge group- or cluster-randomized trials (SWGRTs), aiding public health research. This tool enhances the discovery of specialized study publications.

Keywords:
AIartificial intelligencebiomedicalclinical trialsdatasetdevelopmentdocument classificationlanguage modelmachine learningmodelnatural language processingpublic healthrandomized trialstooltransformertrial

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Area of Science:

  • Biomedical informatics
  • Public health research
  • Artificial intelligence in medicine

Background:

  • Monitoring public health literature is vital but challenging.
  • Identifying specific research designs, like group- or cluster-randomized trials (GRTs), is difficult with current methods.

Purpose of the Study:

  • Develop a fine-tuned language model to identify publications with specific clinical trial designs.
  • Accurately classify studies using group- or cluster-randomized trial (GRT), individually randomized group-treatment trial (IRGT), or stepped wedge group- or cluster-randomized trial (SWGRT) designs.

Main Methods:

  • Fine-tuned the BioMedBERT language model on biomedical literature from the National Institute of Health.
  • Trained the model to classify publications into three nested clinical trial design categories.
  • Evaluated model performance on unseen data for sensitivity and specificity.

Main Results:

  • The model achieved high sensitivity and specificity across all tested classes.
  • Performance metrics included: negatives (0.95, 0.93), GRTs (0.94, 0.90), IRGTs (0.81, 0.97), and SWGRTs (0.96, 0.99).

Conclusions:

  • Fine-tuned, domain-specific language models can accurately identify complex study designs.
  • This model provides a valuable tool for the public health community to find relevant research.
  • Addresses a critical need for efficient identification of specialized public health study designs.