*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: AI for Single-cell Spatially Resolved Multiplex Data
Committee:
Dr. Coskun, Advisor
Dr. Inan, Co-Advisor
Dr. Dyer, Chair
Dr. Qiu
Abstract: The objective of the proposed research is to use machine learning and deep learning for the analysis and understanding of highly multiplex spatially resolved omics data at the cellular and subcellular level. Spatial omics technologies generate highly multiplexed and multimodal single cell data in their native tissue environment. These technologies require novel analysis methods for understanding single cell heterogeneity within their spatial context. This research target to propose AI-based solutions for analyzing highly multiplex spatially resolved single-cell data. In this research we present (1) cross-modality and within-modality translation of multiplex immunofluorescence imaging at subcellular resolution using conditional generative adversarial neural network, (2) graph-based analysis of cellular and subcellular networks of multiplex imaging in cancer tissue, and (3) multimodal data integration of single-cell protein and metabolomic imaging.
