A Deep Learning Framework for Predicting Response to Therapy in Cancer - Sakellaropoulos, Theodore; Vougas, Konstantinos; Narang, Sonali; Koinis, Filippos; Kotsinas, Athanassios; Polyzos, Alexander; Moss, Tyler J; Piha-Paul, Sarina; Zhou, Hua; Kardala, Eleni; Damianidou, Eleni; Alexopoulos, Leonidas G; et al. (Cell reports, 2019)
A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.
Keywords: deep neural networks; Drug Resistance, Neoplasm; precision medicine; individualizovaná medicína; Cell Line, Tumor; Neoplasms; nádory; Deep Learning; lidé; deep learning; Survival Analysis; machine learning; chemorezistence; drug response prediction; DNN; analýza přežití; Precision Medicine; nádorové buněčné linie; Humans