A Deep Learning Framework for Predicting Response to Therapy in Cancer

A Deep Learning Framework for Predicting Response to Therapy in Cancer

Summary: 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 clinic...

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Bibliographic Details
Main Authors: 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, Dimitris Thanos, Kenna R. Mills Shaw, Russell Petty, Aristotelis Tsirigos, Vassilis G. Gorgoulis
Format: Article
Language:English
Published: Elsevier 2019-12-01
Series:Cell Reports
Online Access:http://www.sciencedirect.com/science/article/pii/S2211124719314883

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http://www.sciencedirect.com/science/article/pii/S2211124719314883

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