AI-Driven Spatial Proteomics and Foundation Models for Understanding Tumor Heterogeneity in Prostate Cancer
To Know
Where:
Online
Organizer:
CBIIT
Presented By:
Raghu Machiraju (Ohio State University)
About this Class
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Learn how NIH-funded researchers are addressing emerging challenges in cancer data integration, multimodal artificial intelligence (AI), interpretability, and scalable foundation-model development for precision oncology for prostate cancer.
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The Ohio State University’s Dr. Raghu Machiraju will:
- discuss methods for transforming heterogeneous, prostate cancer data sets into structured representations for AI and foundation models. This includes ways that bring clinical outcome information earlier into the workflow, not later.
- describe his team’s TOPAZ framework for constructing tissue maps and tumor-immune interaction zones from spatial proteomics data.
- share his team’s ongoing work with multimodal, vision-language foundation models. By aligning histopathology images with molecular and spatial descriptors, these models can generate tissue analysis and tumor recurrence predictions.
By better understanding how immune and stromal systems interact with tumor architecture, Dr. Machiraju and his team can better support the study of prostate tumor maintenance, progression, recurrence, and treatment response.