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In Cell reports ; h5-index 119.0

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.

Sakellaropoulos Theodore, Vougas Konstantinos, Narang Sonali, Koinis Filippos, Kotsinas Athanassios, Polyzos Alexander, Moss Tyler J, Piha-Paul Sarina, Zhou Hua, Kardala Eleni, Damianidou Eleni, Alexopoulos Leonidas G, Aifantis Iannis, Townsend Paul A, Panayiotidis Mihalis I, Sfikakis Petros, Bartek Jiri, Fitzgerald Rebecca C, Thanos Dimitris, Mills Shaw Kenna R, Petty Russell, Tsirigos Aristotelis, Gorgoulis Vassilis G


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