Metabolomics has become a powerful and ubiquitous component of modern biological studies, yet experimental results are often hard to interpret, particularly due to sparse metabolome coverage and annotation ambiguity. Pathway based modelling offers a valuable alternative by building models in which the explanatory features - pathway scores - have immediate functional interpretation.
In this webinar, Prof Tim Ebbels (Professor of Biomedical Data Science at Imperial College London) will present his work on building interpretable machine learning models for metabolomics and multi-omics data using pathway and chemical class scores.
This webinar will cover:
How to apply these approaches in many settings, from multi-assay mass spectrometry studies through multi-omics integration to single cell and mass spectrometry imaging studies
An outline of the challenges which these approaches bring and explore attempts to address them
A demonstration of how pathway and class-based modelling approaches hold promise to improve our ability to understand the complex biological signals seen in modern data-rich metabolomics experiments.
Speaker: Prof Tim Ebbels, Professor of Biomedical Data Science, Imperial College London
Date/Time: 13 October 2026, 4 - 5 pm AEDT/ 3 - 4 pm AEST / 3:30 - 4:30 pm ACDT / 1 - 2 pm AWST (check in your timezone)
Who the webinar is for:
This webinar is for life scientists and bioinformaticians who use metabolomics and/or multi-omics data in their work.
How to join:
This webinar is free to join but you must register for a place in advance.
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