Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Molecular Twin Source Code
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Molecular Twin Source Code
Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe an artificial intelligence precision medicine platform, known as the Molecular Twin, and use it to analyze a dataset of 6363 clinical and advanced multi-omic molecular features from patients with resected PDAC applied to machine learning models to accurately predict disease survival (DS). Here we provide the source code for our artificial intelligence precision medicine platform.
machine learning, multi-omic profiling, precision medicine, pancreatic cancer, artificial intelligence, computational pathology
machine learning, multi-omic profiling, precision medicine, pancreatic cancer, artificial intelligence, computational pathology
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