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- W3213145260 abstract "A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10-6), which correspond to DNA damage repair and immune response. We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks." @default.
- W3213145260 created "2021-11-22" @default.
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- W3213145260 date "2021-12-01" @default.
- W3213145260 modified "2023-10-16" @default.
- W3213145260 title "Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients" @default.
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- W3213145260 doi "https://doi.org/10.1016/j.isci.2021.103415" @default.
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