Knowledge Graph and Large Language Model for Metabolomics

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.

Cite

Text

Lu. "Knowledge Graph and Large Language Model for Metabolomics." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35218

Markdown

[Lu. "Knowledge Graph and Large Language Model for Metabolomics." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/lu2025aaai-knowledge/) doi:10.1609/AAAI.V39I28.35218

BibTeX

@inproceedings{lu2025aaai-knowledge,
  title     = {{Knowledge Graph and Large Language Model for Metabolomics}},
  author    = {Lu, Yuxing},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29281-29282},
  doi       = {10.1609/AAAI.V39I28.35218},
  url       = {https://mlanthology.org/aaai/2025/lu2025aaai-knowledge/}
}