Open AccessA Deep Learning Framework for Predicting Response to Therapy in Cancer
Author(s) -
Theodore Sakellaropoulos,
Konstantinos Vougas,
Sonali Narang,
Filippos Koinis,
Athanassios Kotsinas,
Alexander Polyzos,
Tyler J. Moss,
Sarina A. PihaPaul,
Hua Zhou,
Eleni Kardala,
Eleni Damianidou,
Leonidas G. Alexopoulos,
Iannis Aifantis,
Paul A. Townsend,
Mihalis I. Panayiotidis,
Petros P. Sfikakis,
Jiří Bártek,
Rebecca C. Fitzgerald,
Dimitris Thanos,
Kenna Shaw,
Russell Petty,
Aristotelis Tsirigos,
Vassilis G. Gorgoulis
Publication year - 2019
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2019.11.017
Subject(s) - pharmacogenomics , deep learning , artificial intelligence , artificial neural network , machine learning , deep neural networks , cancer medicine , cancer , cancer drugs , computer science , precision medicine , drug response , personalized medicine , cancer therapy , cancer treatment , medicine , drug , bioinformatics , pharmacology , biology , pathology
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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