268 DEGAS: Deep transfer learning reveals cancer-like transcriptional signatures in histologically normal prostate tissue and adjacent-normal tissues in pancreatic cancer
Abstract:OBJECTIVES/GOALS: Single-cell and spatial transcriptomics have revealed high heterogeneity in the tumor and microenvironment. Identifying populations of cells that impact a patient’s prognosis is an important research goal, so researchers can generate hypotheses and clinicians can provide targeted treatment. METHODS/STUDY POPULATION: DEGAS uses deep-transfer-learning to identify patterns between patient tumor RNA-seq and clinical outcomes and map these associations on to higher-resolution data like spatial and… Show more
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