Justin L. Couetil

The paper introduces DEGAS, a method to identify high-risk cells in spatial transcriptomics data by transferring disease attributes to individual cells, validated in cancer and diabetes datasets.

The paper introduces DEGASv2, a deep learning framework for analyzing single-cell and spatial transcriptomics data, revealing disease risk heterogeneity and drug response patterns in multiple conditions.

The paper introduces DEGAS, a transfer learning framework that maps clinical data to spatial transcriptomics in prostate cancer, identifying transcriptional signatures of carcinogenesis in morphologically normal tissues.