The Use of Machine Learning in Computational Metabolomics Workflows

Explore the cutting-edge applications of machine learning in computational metabolomics workflows in this 44-minute seminar by Justin J.J. van der Hooft, Assistant Professor in Computational Metabolomics at Wageningen University. Delve into the challenges of structurally characterizing specialized metabolites and their crucial roles in regulating physiological processes and inter-organism communication. Learn about recent advances in computational metabolomics, including the development of tools like MS2LDA, MotifDB, Spec2Vec, MS2DeepScore, and MS2Query for substructure discovery, annotation, and improved spectral similarity scoring. Gain insights into the integration of genome and metabolome mining workflows to accelerate specialized metabolite discovery and characterization. Understand the potential impact of these methodological developments on our understanding of metabolites' roles in growth, development, and health. Discover the speaker's perspective on the importance of data sharing in advancing the field of metabolomics.

Justin J.J. van der Hooft: The use of Machine Learning in Computational Metabolomics Workflows