Predicting Cell Fate and State from RNA and Protein Spatial Maps

Integrates high-resolution spatial transcriptomics and proteomics data to predict cellular fate. Develops computational frameworks for registering multi-modal spatial images, harmonizing RNA-to-protein measurements, and training AI models to identify biophysical fingerprints of cell states.

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Author Jean Fan
Last Updated September 10, 2026, 18:10 (UTC)
Published August 5, 2026, 18:10 (UTC)
Citation Jean Fan 2026. Predicting Cell Fate and State from RNA and Protein Spatial Maps. CyVerse Data Commons.
Description Integrates high-resolution spatial transcriptomics and proteomics data to predict cellular fate. Develops computational frameworks for registering multi-modal spatial images, harmonizing RNA-to-protein measurements, and training AI models to identify biophysical fingerprints of cell states.
PublicationYear 2026
Publisher CyVerse Data Commons
Rights This material is based upon work supported by the U.S. National Science Foundation under Award No. #2335029. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.
Subject spatial omics, RNA protein imaging, cell state prediction, machine learning, subcellular organization, transcriptomics
de_created_date 2026-03-04T17:24:24Z
de_modified_date 2026-07-28T22:07:47Z