A deep learning framework for predicting response to therapy in cancer
Authors
Sakellaropoulos, TVougas, K
Narang, S
Koinis, F
Kotsinas, A
Polyzos, A
Moss, TJ
Piha-Paul, S
Zhou, H
Kardala, E
Damianidou, E
Alexopoulos, LG
Aifantis, I
Townsend, Paul A
Panayiotidis, MI
Sfikakis, P
Bartek, J
Fitzgerald, RC
Thanos, D
Mills Shaw, KR
Petty, R
Tsirigos, A
Gorgoulis, Vassilis G
Affiliation
Department of Pathology, NYU School of Medicine, New York, NY 10016, USAIssue Date
2019
Metadata
Show full item recordAbstract
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.Citation
Sakellaropoulos T, Vougas K, Narang S, Koinis F, Kotsinas A, Polyzos A, et al. A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Rep. 2019;29(11):3367-73 e4.Journal
Cell ReportsDOI
10.1016/j.celrep.2019.11.017PubMed ID
31825821Additional Links
https://dx.doi.org/10.1016/j.celrep.2019.11.017Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.celrep.2019.11.017
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