Biologically informed deep neural network for prostate cancer discovery - Elmarakeby, Haitham A.; Hwang, Justin; Arafeh, Rand; Crowdis, Jett; Gang, Sydney; Liu, David; AlDubayan, Saud H.; Salari, Keyan; Kregel, Steven; Richter, Camden; Arnoff, Taylor E.; Park, Jihye; et al. (Nature (London), 2021)
Keywords: 119/118; 13; 13/1; 631/67/69; 639/705/117; Artificial neural networks; Cell cycle; Cell Cycle Proteins - genetics; Cell growth; Deep Learning; Diagnosis; Drug Resistance, Neoplasm - drug effects; Drug Resistance, Neoplasm - genetics; Fibroblast growth factor receptor 1; Genomics; Health aspects; Humanities and Social Sciences; Humans; Learning algorithms; Machine learning; Male; Metastasis; Modelling; multidisciplinary; Mutation; Neural networks; Patients; Phenotypes; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - drug therapy; Prostatic Neoplasms - genetics; Proto-Oncogene Proteins - genetics; Receptor, Fibroblast Growth Factor, Type 1 - genetics; Receptors, Androgen - genetics; Reproducibility of Results; Science; Science (multidisciplinary); Therapeutic targets; Treatment resistance; Tumor Suppressor Protein p53 - genetics; Tumors; United States