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    A deep learning framework for predicting response to therapy in cancer

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    Authors
    Sakellaropoulos, T
    Vougas, 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
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    Affiliation
    Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
    Issue Date
    2019
    
    Metadata
    Show full item record
    Abstract
    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 Reports
    URI
    http://hdl.handle.net/10541/622683
    DOI
    10.1016/j.celrep.2019.11.017
    PubMed ID
    31825821
    Additional Links
    https://dx.doi.org/10.1016/j.celrep.2019.11.017
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.celrep.2019.11.017
    Scopus Count
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    All Paterson Institute for Cancer Research

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