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UID:20250312T165924EDT-4331Nwddmg@132.216.98.100
DTSTAMP:20250312T205924Z
DESCRIPTION:David Zhang\, PhD\n\nAssistant Professor\n	Department of Biostat
 istics and Genetics | UNC-Chapel Hill\n	\n	WHEN: Wednesday\, April 2\, 2025\
 , from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 9IÖÆ×÷³§Ãâ·Ñ College Avenue\, Roo
 m 1140\; Zoom\n	NOTE: David Zhang will be presenting from Chapel Hill\n\nAb
 stract\n\nSpatial transcriptomics (STs) has demonstrated enormous potentia
 l for generating intricate molecular maps of cells within tissues. Despite
  the availability of many ST platforms\, none of them provides a comprehen
 sive solution. An ideal ST platform should achieve single-cell resolution\
 , cover the whole transcriptome\, and be cost-effective. While such ST dat
 a are difficult to collect physically using existing platforms\, they can 
 be constructed in silico using innovative machine learning algorithms. Her
 e we present iStar\, a generative computer vision model that integrates lo
 w-resolution ST measurements with high-resolution histology images to cons
 truct spatial gene expression data at near-single-cell-resolution. The res
 ulting model not only enhances gene expression resolution but also enables
  gene expression prediction in tissue sections where only histology images
  are available. The application of iStar to healthy and diseased samples a
 cross diverse datasets demonstrates its efficacy in facilitating scientifi
 c inquiries and performing clinical tasks\, including tissue segmentation\
 , cell type inference\, cancer detection\, and tumor microenvironment anal
 ysis\, all with state-of-the-art accuracy and efficiency. Using this tool\
 , researchers and physicians can detect and analyze diseases such as cance
 r with unprecedented precision and depth.\n\nSpeaker Bio\n\nDavid Zhang is
  an Assistant Professor of Biostatistics and Genetics at the University of
  North Carolina\, Chapel Hill. His research centers around the development
  of novel machine learning and AI frameworks for analyzing high-dimensiona
 l\, multi-modal\, and multi-scale data\, especially those originating from
  spatial omics\, computational pathology\, and medical imaging. The overar
 ching goal of his research is to harness the power of AI to answer the mos
 t pressing scientific inquiries and clinical needs.\n
DTSTART:20250402T193000Z
DTEND:20250402T203000Z
SUMMARY:Inferring Super-Resolution Tissue Architecture by Integrating Spati
 al Transcriptomics with Histology
URL:/spgh/channels/event/inferring-super-resolution-ti
 ssue-architecture-integrating-spatial-transcriptomics-histology-363847
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