NCI-funded Machine Learning Gives New Insight into Endometrial Cancer

Panoptes, a new machine learning model built on a deep convolutional neural network, is proving effective and efficient in reading histological slides from NCI genomic and proteomic data sets to predict four subtypes of endometrial cancer. Dr. David Fenyö and team from New York University’s Langone Health published their findings in a new article in Cell Reports Medicine.

Panoptes, named for a 100-eyed giant in Greek mythology, offers new insight into existing, archived histological slides stored in NCI’s Clinical Proteomic Tumor Analysis Consortium and The Cancer Genome Atlas data sets.

As noted by the investigators, “Panoptes predicted histological and molecular subtypes in endometrial cancer, as well as the mutation status of critical genes, with remarkable precision.”

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