DeepDEP: deep learning of a cancer dependency map using cancer genomics



Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells, or to unscreened cell lines and further to tumors. DeepDEP is a deep learning model that predicts cancer dependencies using integrative genomic profiles. It employs unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. An R package, Prep4DeepDEP, is available at GitHub to generate the input genomic and gene fingerprint data from user’s genomic datasets.
Date made available2021
PublisherCode Ocean

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