Parag Mallick

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 study uses a computational simulation to show that mutualism enhances immune evasion in slow-growing tumors, increasing their survival chances compared to tumors without mutualism.

This study identifies the temporal order of cancer hallmarks during tumorigenesis using mutation allele frequency and selective advantage across cancer and normal tissues.

This paper introduces a theoretical framework called PrISM for proteome-scale single-molecule protein identification using multi-affinity reagents and demonstrates its high coverage and dynamic range in simulations.

The paper introduces a geostatistical approach to visualize tumor heterogeneity and spatial patterns in the tumor microenvironment using agent-based models of ecological interactions.