Professional

Multimodal Deep Learning & Spatial Analysis in Ovarian Cancer

Overview This project focused on the development of a comprehensive computational pathology framework to investigate molecular signatures in ovarian cancer. The objective was to bridge the gap between unstructured histological data and patient-level clinical endpoints. The solution involved an automated pipeline for registering serial tissue sections (H&E and IHC) and a downstream analysis workflow utilizing foundation models to correlate morphological features with molecular data, including Response, Mismatch Repair (MMR) status, and Tumor Mutational Burden (TMB).

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Multi-omics R Shiny Dashboard

Overview Built a multi-omics R Shiny dashboard at Regeneron to bridge the gap between transcriptomic data and digital pathology metrics, enabling researchers to explore spatial and molecular relationships in cancer.

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