Deep learning: shaping the medicine of tomorrow

Konstantinos Vougas; Spyridon Almpanis; Vassilis Gorgoulis

doi:10.1080/23723556.2020.1723462

Deep learning: shaping the medicine of tomorrow

Mol Cell Oncol. 2020 Feb 19;7(3):1723462. doi: 10.1080/23723556.2020.1723462. eCollection 2020.

Authors

Konstantinos Vougas^{1

2}, Spyridon Almpanis¹, Vassilis Gorgoulis^{1

2

3

4}

Affiliations

¹ Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
² Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
³ Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester, UK.
⁴ Centre for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece.

Abstract

Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow.

Keywords: Personalized medicine; cancer treatment; deep neural networks (DNN); drug response prediction; machine learning; metastasis detection.

Grants and funding

Financial support was from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grants agreement No. 722729 (SYNTRAIN); the Welfare Foundation for Social & Cultural Sciences (KIKPE), Greece; Pentagon Biotechnology Ltd, UK; DeepMed IO Ltd, UK, NKUA-SARG grants No 70/3/9816, 70/3/12128.