Topic: Interpretable machine learning

Integrating and Interpreting Metabolomics and Multi-omics Data with Pathway and Class Based Models

Tim Ebbels presents interpretable machine-learning models for metabolomics and multi-omics using pathway and chemical-class scores. Sparse coverage of the metabolome and uncertain annotations make conventional results difficult to interpret; pathway-based features aim to connect model output directly to biological function. The webinar examines applications from multi-assay mass spectrometry and multi-omics integration to single-cell and mass-spectrometry imaging, discusses methodological challenges and attempts to resolve them, and demonstrates how these approaches can clarify complex biological signals. Online via Zoom. Free for interested life scientists and bioinformaticians, with advance registration required through the Australian BioCommons event page. Tuesday 13 October, 16:00–17:00 AEDT (Australia/Melbourne), equivalent to 05:00–06:00 UTC. The event is subject to the BioCommons Code of Conduct and may be recorded.