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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.

S Solomon Darnell¹, Rupert W Overall^1,2, Andrea Guarracino¹

¹University of Tennessee Health Science Center, Memphis, TN, USA.

Biorxiv : the Preprint Server for Biology

|October 28, 2024

Summary

We developed GNQA, a generative pre-trained transformer (GPT) knowledge base using retrieval augmented generation (RAG) for aging and dementia research. This system enhances response accuracy and provides verifiable references, achieving high user satisfaction and automated relevance scores.

Keywords:

FAIR GPT RAG aging, dementia, Alzheimer’s and diabetes artificial intelligence systems genetics

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Area of Science:

Biomedical Informatics
Artificial Intelligence in Medicine
Gerontology and Neurodegenerative Diseases

Background:

Generative AI models like GPT can produce inaccurate information ('hallucinations').
Retrieval Augmented Generation (RAG) systems require robust evaluation methods.
Aging, dementia, Alzheimer's disease, and diabetes research necessitates reliable knowledge bases.

Purpose of the Study:

To develop and evaluate GNQA, a GPT-based knowledge base utilizing RAG for aging and dementia research.
To implement a context provenance tracking mechanism to ensure response accuracy and traceability.
To introduce RAGAS, an automated system for evaluating RAG performance, combining human and AI assessments.

Main Methods:

A corpus of 3,000 peer-reviewed publications on aging, dementia, Alzheimer's, and diabetes was integrated into a RAG system.
A context provenance tracking mechanism was developed to link responses to source material.
The RAG Assessment System (RAGAS) was created, incorporating human expert and citizen scientist feedback alongside AI-driven evaluation.

Main Results:

Human respondents approved GNQA responses 76% of the time.
RAGAS achieved 90% answer relevance for expert-posed questions and 74% for GPT-generated questions.
The study established RAGAS as a benchmark for continuous RAG system performance assessment.

Conclusions:

GNQA, powered by RAG and enhanced with provenance tracking, offers a reliable and verifiable knowledge resource for aging and dementia research.
RAGAS provides a robust framework for evaluating RAG systems, crucial for mitigating AI-generated inaccuracies.
GNQA is accessible via the open-source GeneNetwork.org web service, promoting broader research accessibility.