Research Area:  Machine Learning
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.
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Author(s) Name:  Theodore Sakellaropoulos, Konstantinos Vougas, Sonali Narang, Filippos Koinis, Athanassios Kotsinas, Alexander Polyzos, Tyler J. Moss, Sarina Piha-Paul, Hua Zhou, Eleni Kardala, Eleni Damianidou, Leonidas G. Alexopoulos, Iannis Aifantis, Paul A. Townsend, Mihalis I. Panayiotidis, Petros Sfikakis, Jiri Bartek, Rebecca C. Fitzgerald, Vassilis G. Gorgoulis
Journal name:  Cell Reports
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Publisher name:  ELSEVIER
DOI:  10.1016/j.celrep.2019.11.017
Volume Information:  Volume 29, Issue 11, 10 December 2019, Pages 3367-3373.e4
Paper Link:   https://www.sciencedirect.com/science/article/pii/S2211124719314883