Gregory W. Charville

This study identifies HA and CD44 as mediators of matrix changes and fibrosis in PSC, showing their targeting can reverse disease progression.

Eva is a foundation model for tissue imaging data that learns multi-scale spatial representations and performs various tasks like cross-modal inference and patient stratification.

This paper uses single-nuclei RNA sequencing and spatial transcriptomics to characterize KPGCT, showing neoplastic cells express HMGA2::NCOR2 fusions and activate the hippo pathway, driving CSF1 production and macrophage recruitment.

This study uses single-cell and spatial transcriptomics to identify distinct tumor states in uterine leiomyosarcomas and proposes drug targets based on these states.

ROSIE generates multiplex immunofluorescence staining from H&E images by imputing protein expression and localization using deep learning.

The paper develops a spatiotemporal multi-omics platform and machine-learning tool to manufacture hPSC-derived esophageal mucosa.

7-UP generates in silico 40-plex CODEX data from a 7-plex mIF panel using cellular morphology, enabling cell type classification and survival prediction across diverse samples.

SPACE-GM identifies spatial motifs in cancer samples linked to recurrence and immunotherapy outcomes using geometric deep learning.