Knowledge-based machine learning to extract disease mechanisms from multi-omics data
Mercoledì 15 Maggio 2024
16:00
Online su TEAMS
Speaker
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Julio Saez-RodriguezBioquant, University of Heidelberg
Prof. Julio Saez-Rodriguez - Bioquant, University of Heidelberg
Knowledge-based machine learning to extract disease mechanisms from multi-omics data
Abstract
Omics approaches, in particular those with single-cell and 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. In this talk, I will present resources and methods from our group to 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.
Omics approaches, in particular those with single-cell and 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. In this talk, I will present resources and methods from our group to 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.
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