Knowledge-Based Machine Learning to Extract Molecular Mechanisms from Single-Cell and Spatial Multi-Omics

Single-cell and spatially resolved omics technologies provide unique opportunities to the key study intra- and inter-cellular processes that drive immunological systems and their deregulation in disease. 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 that combine multi-omic single cell and spatial data with biological knowledge and illustrate them on medically relevant cases. For more information visit: https://saezlab.org/ Moderator: James A. Glazier, PhD, Indiana University To view the slides for this video, please visit: https://drive.google.com/file/d/19DhBnf9pwGmtGKORhEmvMa1mz7tMpoT4 If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/