Deep Learning in Systems Medicine

Haiying / HY Wang, Estelle Pujos-Guillot, Blandine Comte, Joao Luis de Miranda, , Ivan Chorbev, Filippo Castiglione, Paolo Tieri, Steven Watterson, Roisin Mc Allister, Tiago De Melo Malaquias, Massimiliano Zanin, Taranjit Singh Rai, Huiru Zheng

Research output: Contribution to journalArticlepeer-review

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Abstract

Systems Medicine has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention, and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, Deep Learning holds great promise in this endeavour. This review paper addressed the main developments of Deep Learning algorithms and a set of general topics where Deep Learning is decisive; namely, within the Systems Medicine landscape. It discusses how Deep Learning can be applied to Systems Medicine with an emphasis on the applications to predictive, preventive, and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of Deep Learning in Systems Medicine, one of them is involving the creation of a model for personalised Parkinson's disease. The review offers valuable insights and informs the research in Deep Learning and Systems Medicine.
Original languageEnglish
JournalBriefings in Bioinformatics
Publication statusAccepted/In press - 26 Aug 2020

Keywords

  • Deep Learning (DL)
  • Systems Medicine (SM)
  • Data integration
  • Biomarker discovery
  • disease classification

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