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...

Genetic Lingo

Genetic Lingo

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

You might also read

Related Articles

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

Sort by

Same author

Assessing the translational relevance of specific molecular pathways in spontaneous lupus mouse models.

Frontiers in immunology·2026

Same author

JUN Specifies the Local Differentiation and Maintenance of Pro-tumoral Monocyte-Derived Macrophages.

Cancer research·2026

Same author

Inflammation-Driven Lymphoid Structures: Organization, Function, and Clinical Impact Across Autoimmunity, Cancer, and Checkpoint Toxicity.

Immunological reviews·2026

Same author

A Three-Gene Interferon Signature Predicts Sustained Complete Remission in Pediatric AML Patients.

Cancers·2026

Same author

Human LFA-1 governs T cell immune surveillance of the skin.

Science immunology·2026

Same author

Male-biased Yap1-Cd276/B7-H3 axis for immune evasion in medulloblastoma.

Cancer cell·2026

Search research articles

Related Experiment Video

Updated: Jul 13, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Harnessing large language models (LLMs) for candidate gene prioritization and selection.

Mohammed Toufiq¹, Darawan Rinchai², Eleonore Bettacchioli^3,4

¹The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

Journal of Translational Medicine

|October 16, 2023

Summary

Large language models (LLMs) can efficiently prioritize genes for clinical insights by leveraging biomedical knowledge. This study demonstrates LLMs

Keywords:

Erythroid cells Feature selection Generative artificial intelligence Large language models Transcriptomics

More Related Videos

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

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Related Experiment Videos

Last Updated: Jul 13, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Feature selection is crucial for translating systems-scale molecular profiling into clinical applications.
Knowledge-driven gene selection methods struggle with vast biomedical information.
Large language models (LLMs) offer a potential solution for efficient knowledge extraction.

Purpose of the Study:

To assess the utility of LLMs for knowledge-driven gene prioritization and selection.
To establish and evaluate a workflow for LLM-assisted gene prioritization.

Main Methods:

Evaluated four leading LLMs on tasks related to gene prioritization.
Developed a workflow involving LLM-based functional convergence identification, gene scoring, and justification.
Incorporated fact-checking and transcriptome profiling data for final gene selection.

Main Results:

GPT-4 and Claude demonstrated superior performance among evaluated LLMs.
The LLM-driven workflow successfully prioritized candidate genes for erythroid cell modules.
LLMs provided validated justifications, with GPT-4 revising its top gene choice upon data integration.

Conclusions:

LLMs can effectively prioritize candidate genes with reduced human intervention.
This technology has the potential to significantly enhance productivity in biomedical research.
LLMs show promise for tasks requiring the synthesis of extensive biomedical knowledge.