Knowledge-based machine learning to extract disease mechanisms from spatial multi-omics
Trainer: Julio Saez Rodriguez
Overview: Omics approaches, in particular those with spatial resolution, provide unique opportunities to study the deregulation of intra- and inter-cellular processes in disease using computational approaches. The use of prior biological knowledge allows us to reduce the dimensionality and increase the interpretability of the data, in particular by extracting from the data features describing the activity of molecular processes such as signaling pathways, gene regulatory networks, and cell-cell communication events. This presentation highlights resources and methods from that effectively capture and deploy prior knowledge from the public domain to extract mechanistic information from omics data using computational methods, and illustrate them in several disease applications.
Learning outcomes
By the end of this lecture you will be able to:
- Familiarise yourself with machine learning approaches applicable in extracting mechanistic information from omics data
- Identify a few useful resources and methods in this domain
Materials: