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Activity Number: 496 - Machine Learning Methods for Single-Cell Analysis
Type: Invited
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract #320647
Title: Statistical Machine Learning Models for Large-Scale Spatial Omics
Author(s): James Zou*
Companies: Stanford University
Keywords: single cell; spatial biology; machine learning; spatial statistics
Abstract:

Recent technical advances enable us to measure spatially resolved transcriptome and proteome at single-cell resolution. This opens up exciting opportunities to characterize the spatial organization of cells and molecules, and link spatial motifs to disease phenotypes. This talk will provide an overview of statistical and computational challenges that arise from large-scale spatial omics data. I will then discuss how to extend ideas from graph neural networks, computer vision and spatial point processes to tackle these new questions.


Authors who are presenting talks have a * after their name.

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