Raghu Machiraju

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

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 paper introduces GENN, a method for multi-omic interactome analysis that improves prediction of biomedical outcomes by learning salient associations in a low-dimensional space.

The paper integrates mouse and human single-cell RNA sequencing data to predict and spatially map cell-type composition in human brain tissue.