Metabolomics

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.

Farhad Danesh examines how mitochondrial reactive oxygen species, altered metabolism and structural remodelling convert hyperglycaemia into progressive kidney injury. The seminar connects mitochondrial dynamics with diabetic microvascular damage, non-coding RNA regulation and experimental models that establish causal links to disease progression. Genome-wide CRISPR screens and spatial metabolomics reveal cellular gene-regulatory and metabolic changes. Integrating these approaches with molecular genetics, genomics and epigenomics, the work seeks therapeutic targets to prevent diabetic kidney disease progressing to kidney failure. Hybrid: New Research Building Auditorium, Georgetown University Medical Center, Washington, DC, and Zoom. Thursday 24 September 2026, 16:00–17:00 EDT (America/New_York; UTC−4). Use the public event registration form to arrange attendance, or follow its Zoom link for remote participation. Organized by Georgetown’s Distinguished Scientist Seminar Series. Danesh is Professor of Medicine and Chief of Nephrology at MD Anderson.

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.