Biologically informed deep neural network for prostate cancer discovery

By Haitham A. Elmarakeby, Justin H Hwang, Rand Arafeh, J. Crowdis, Sydney Gang, David Liu, S. AlDubayan, K. Salari, Steven Kregel, Camden Richter and 4 more. Nature, 2021

390 citations recorded by the source corpus.

The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics3–5. 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-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types. A biologically informed, interpretable deep learning model has been developed to evaluate molecular drivers of resistance to cancer treatment, predict clinical outcomes and guide hypotheses on disease progression.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/2b24bb6cbfa2b3118b01a47aebf58308f4eb8ad5

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In Molecular Biology

Citations in the corpus

Obtaining genetics insights from deep learning via explainable artificial intelligence (2022, methodology): “In a similar spirit to DCell and P-NET, others have created neural networks in which units and parameters have a biophysical interpretation 80,81 .”