2025-W9: Metabolomics Data Standardization and Harmonization for AI

Presenters:
Rima Kaddurah-Daouk (Duke University), Session Overview and Goals
Rick Dunn (University of Liverpool, UK), Metabolomics Standards Initiative (MSI) -I, 2007 and Launch of MSI -2
Jennifer Kirwan (Berlin Institute of Health at Charite Universitatsmedizin), mQACC perspective and Lessons learned from cohort studies
Xianlin Han (UT Health – San Antonio), Lipidomics Standards Initiative and Outcomes
Peter Meikle (Baker Heart and Diabetes Institute), Harmonization of Lipidomics Datasets and Novel approaches – from lipidomics to metabolomics
Thomas Hankemeier (Leiden University) and the Dutch Team, FAIR Data Machine Readable and AI Ready
Tuulia Hyötyläinen (Örebro University) and Team Members, Exposomics Data and Its Integration with Metabolomics and Lipdomics data
Moderators, Speakers, and All, Discussion: Future concept, data visitation, and AI applications

Euretos Moderators/Discussants:
David Wishart, University of Alberta
Matej Orešič, Örebro University
Tuulia Hyötyläinen, Örebro University
Susan Sumner, UNC Chapel-Hill

Description:
This workshop brings together leaders in large initiatives focused on integrating metabolomics and lipidomics data across studies, assays, laboratories, and repositories. Starting with updated recommendations from the Metabolomics Standards Initiative, the workshop will explore optimizing large-scale data use in open biorepositories, focus on standardizing lipid annotations and quantifications, and incorporate new approaches for data harmonization and FAIRification to coalesce different data types and existing knowledge. Finally, it will explore using digital twinning and AI to search across data resources. This workshop sets the stage for broader community discussions that include metabolomics and lipidomics societies, large initiatives, task groups, and interest groups to define the next steps in data reporting, integration, harmonization, data sharing, and visiting strategies.

Workshop Objectives:
 Metabolomics Lipidomics data standardization and harmonization complexities
 getting data ready for AI
 connecting biorepositories
 future data visitation

Learning Outcomes:
 better practices in metabolomics data and its reporting
 data harmonization approaches
 machine readable data for AI applications

Видео 2025-W9: Metabolomics Data Standardization and Harmonization for AI канала Metabolomics Society