Events
FDS Statistics & Data Science Seminar
Machine learning for aging and spatial omics
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Speaker: Eric Sun (Stanford) PhD Student Stanford University Monday, January 27, 2025 11:30AM - 1:00PM Lunch at 11:30am in room 1307
Talk from 12:00-1:00pm in room 1327A and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=092a7568-7c71-47b0-b0da-b233012bcd25 |
Abstract: Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have enabled the high-dimensional profiling of complex biology including that underlying aging. As such, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First, I present the development of high-resolution machine learning models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks, I discovered that some cell types can dramatically influence the aging of nearby cells. Next, I present new computational and statistical methods for overcoming the gene coverage limitations of existing spatial omics technologies, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging. Finally, I introduce several methods for improving the reliability and robustness of high-dimensional data visualizations.
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