Elizabeth McDonough

The paper investigates cell engulfment in colorectal cancer, showing it creates competitive metabolic niches linked to clinical outcomes.

Prostate-TriMod integrates visual morphology, spatial tissue maps, and clinical outcomes for prostate cancer to enable multimodal AI research.

This paper identifies a five-protein stem cell signature using a deep neural network that predicts relapse in stage III colorectal cancer patients.

This paper introduces PhenoMol, a framework that integrates multiomic and multi-phenotypic data to identify biological pathways linked to physical fitness, outperforming traditional regression models.

The paper develops TOPAZ, a tool that predicts cancerous cells/glands and analyzes immune cell spatial relationships in prostate cancer, finding that immune cell distance from cancer glands correlates with recurrence risk.

This study uses high-resolution spatial proteomics to characterize colorectal cancer consensus molecular subtypes at single-cell resolution, revealing subtype-specific immune and metabolic features.

The paper uses multiplexed immunofluorescence and AI models to analyze the tumor microenvironment of diffuse midline glioma and predict antigens from H&E staining.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

This study compares tumor cell phenotype and heterogeneity differences between IDH1 mutant and wild-type gliomas using integrated multi-omics data.