Deep Learning in Systems Medicine

Haiying / HY Wang, Estelle Pujos-Guillot, Blandine Comte, Joao Luis de Miranda, Vojtech Spiwok, 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 journalReview articlepeer-review

22 Citations (Scopus)
208 Downloads (Pure)


Systems medicine (SM) 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 (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM 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 DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

Original languageEnglish
Pages (from-to)1543-1559
Number of pages17
JournalBriefings in Bioinformatics
Issue number2
Early online date16 Nov 2020
Publication statusPublished (in print/issue) - 1 Mar 2021

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved.


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


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