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Genome-wide Association Studies-GWAS01:11

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...

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Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled,

Ahmed Abdeen Hamed1, Luis M Rocha2

  • 1Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; School of Systems Science & Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA.

STAR Protocols
|May 14, 2026
PubMed
Summary

We developed a protocol to assess ChatGPT's ability to generate reliable biomedical associations. This method uses ontologies and literature, with Retrieval-Augmented Generation (RAG) to verify information and detect AI hallucinations.

Keywords:
Computer sciencesHealth sciencesgenetics

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Medicine
  • Natural Language Processing

Background:

  • Large language models (LLMs) like ChatGPT show potential in biomedical research.
  • Evaluating the accuracy of LLM-generated biomedical associations is crucial.
  • Existing methods face limitations in validating complex biological relationships.

Purpose of the Study:

  • To present a robust protocol for evaluating ChatGPT's capacity to generate disease-centric biomedical associations.
  • To establish a framework for validating biological entities and their associations using established biomedical ontologies and scientific literature.
  • To introduce a self-consistency strategy for assessing the reliability of generative AI models.

Main Methods:

  • Generating disease-centric biomedical associations using ChatGPT.
  • Validating biological entities via biomedical ontologies (e.g., Gene Ontology, Disease Ontology).
  • Verifying generated associations through comprehensive literature searches.
  • Implementing a self-consistency check across different ChatGPT model versions.
  • Utilizing Retrieval-Augmented Generation (RAG) with open-source LLMs for semantic verification to overcome ontology exact-match limitations.

Main Results:

  • The protocol successfully outlines steps for generating, validating, and verifying biomedical associations.
  • A self-consistency strategy was developed to assess the reliability of ChatGPT's outputs.
  • A RAG-powered semantic verification workflow demonstrated the ability of LLMs to establish truth over other LLMs' content.
  • The RAG approach effectively addressed limitations of exact-match searches in biomedical ontologies.
  • The protocol facilitates the detection of AI-generated hallucinations in biomedical data.

Conclusions:

  • The presented protocol offers a systematic approach to evaluate LLM-generated biomedical associations.
  • Semantic verification using RAG-enabled LLMs enhances the reliability of biomedical knowledge discovery.
  • This framework is essential for the responsible integration of AI in biomedical research and clinical applications.
  • The protocol aids in identifying and mitigating AI-generated inaccuracies, ensuring higher data integrity.