Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors.
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DOI: 10.1038/s41467-021-27017-w
Abstract: Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic… read more here.
Keywords: risk; machine learning; learning genomic; primary tumors ... See more keywords