A Deep Learning Framework for Predicting Response to Therapy in Cancer

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 ShawRussell Petty, Aristotelis Tsirigos, Vassilis G. Gorgoulis

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Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response.

Original languageEnglish
Pages (from-to)3367-3373.e4
Number of pages12
JournalCell Reports
Issue number11
Publication statusPublished - 10 Dec 2019


  • deep neural networks
  • DNN
  • drug response prediction
  • machine learning
  • precision medicine

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    Cite this

    Sakellaropoulos, T., Vougas, K., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., Moss, T. J., Piha-Paul, S., Zhou, H., Kardala, E., Damianidou, E., Alexopoulos, L. G., Aifantis, I., Townsend, P. A., Panayiotidis, M. I., Sfikakis, P., Bartek, J., Fitzgerald, R. C., Thanos, D., ... Gorgoulis, V. G. (2019). A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Reports, 29(11), 3367-3373.e4. https://doi.org/10.1016/j.celrep.2019.11.017