Biologically informed deep neural network for prostate cancer discovery.

ABSTRACT: The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge^1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics^3-5. Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NE

SUBMITTER: Elmarakeby HA 

PROVIDER: S-EPMC8514339 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature