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Oral Sessions
Day 3, June 24（Tue.） 13:55-14:10
Room B (Maesato Center)
- 3B-O2-1355
Network-based Integration of Cross-Study Metabolomics Data
(1Kyushu Inst. Tech., 2RIKEN, 3Keio Univ., 4HMT, 5Tokyo Univ. Agr. Tech., 6Kitasato Univ.)
oEisuke Hayakawa1,2, Kozo Nishida2, Mikiko Takahashi2, Rira Matsuta3, Takaki Oka5, Hiroyuki Yamamoto4, Hiroshi Tsugawa2,5, Shin Kawano6
Mass spectrometry-based metabolomics has produced vast datasets, but differences in analytical settings and insufficient metadata curation limit cross-study comparability and integration
To address this, we are developing a platform for integrating and reanalyzing metabolomics data. Our approach utilizes the iDMET method, which quantifies metabolite change trends across studies, making cross-study comparisons possible despite differences in analytical settings. Additionally, we leverage large language models (LLMs) to systematically extract metadata from studies, generating structured summaries that incorporate standardized ontologies. This enhances data annotation and enables more consistent cross-study interpretation.
We construct a network where nodes represent studies and edges link them based on metabolite changes or metadata, uncovering hidden relationships and enabling a more comprehensive exploration of metabolomics research. We are planning to develop an online database to integrate diverse datasets, support collaboration, and drive new hypothesis generation in metabolomics research.