Knowledge Graph and Large Language Model for Metabolomics

Authors

  • Yuxing Lu Department of Big Data and Biomedical AI, College of Future Technology, Peking University Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Tencent AI Lab

DOI:

https://doi.org/10.1609/aaai.v39i28.35218

Abstract

The advancements in Knowledge Graphs (KGs) and Large Language Models (LLMs) are driving transformative changes across various research fields, including metabolomics. These tools present exceptional opportunities to elucidate complex metabolic pathways and identify biomarkers essential to biological systems. My research focuses on harnessing the potential of KGs and LLMs within metabolomics, specifically making interactions between them and with biological researches. KGs, with their structured representation of metabolic entities and relationships, provide a robust foundation for managing extensive multimodal metabolomic knowledge. Recently, I developed a metabolite-centric knowledge graph and explored innovative methodologies to leverage KGs and LLMs for enhancing predictive modeling in clinical settings. My future research aims to fully exploit the capabilities of KGs and LLMs in metabolomics, advancing our understanding and applications in this field.

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Published

2025-04-11

How to Cite

Lu, Y. (2025). Knowledge Graph and Large Language Model for Metabolomics. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 29281-29282. https://doi.org/10.1609/aaai.v39i28.35218