Related Experiment Video
Updated: Jan 11, 2026
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
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Viral Diseases Explorer: a webtool to identify viral disease information derived from multiple LLMs
Oscar Rojas Labra1, Carla M Martinez-Garcia2, Nelly Santoyo-Rivera2
1The Hormel Institute, University of Minnesota, Austin, MN 55912, United States.
Bioinformatics (Oxford, England)
|November 9, 2025
Summary
This study leverages AI to identify diseases caused by over 165,000 viruses, revealing 2833 unique diseases and 5503 hosts. A new webtool, Viral Diseases Explorer, aids in searching these viral disease connections.
Area of Science:
- Virology
- Bioinformatics
- Computational Biology
Background:
- The rapid increase in cataloged viruses presents challenges in identifying associated diseases and hosts.
- Accurate viral disease and host identification is crucial for public health and research.
Purpose of the Study:
- To develop an AI-driven method for identifying diseases caused by viruses and their hosts.
- To create a user-friendly web tool for exploring viral disease information.
Main Methods:
- Employed AI-powered tools to analyze data for 165,363 viruses.
- Utilized Large Language Models (LLMs) to confirm disease designations.
- Developed the Viral Diseases Explorer webtool for data querying.
Main Results:
- Successfully obtained disease information for 165,363 viruses.
- Identified 2833 unique diseases and 5503 distinct hosts.
- Consensus diseases were confirmed by three LLMs.
Conclusions:
- The AI-driven approach effectively identifies viral diseases and hosts.
- The Viral Diseases Explorer tool provides a valuable resource for researchers.
- This work facilitates a better understanding of virus-host-disease relationships.
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