@article{3078814,
    title = "Deep learning: shaping the medicine of tomorrow",
    author = "Vougas, K. and Almpanis, S. and Gorgoulis, V.",
    journal = "Molecular and Cellular Oncology",
    year = "2020",
    volume = "7",
    number = "3",
    publisher = "Taylor and Francis Ltd.",
    doi = "10.1080/23723556.2020.1723462",
    keywords = "biological marker, artificial intelligence;  breast cancer;  cancer staging;  cancer therapy;  carcinogenesis;  data mining;  deep learning;  deep neural network;  drug response;  electronic health record;  follow up;  gene expression;  gene mutation;  genomic instability;  high throughput sequencing;  histopathology;  human;  imaging;  knowledge;  lymph node metastasis;  machine learning;  methylation;  molecular fingerprinting;  Note;  overall survival;  personalized medicine;  quality of life;  support vector machine;  treatment response;  tumor microenvironment;  whole slide imaging",
    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. © 2020, © 2020 Taylor & Francis Group, LLC."
}