Deep learning in systems medicine
2020
Wang, Haiying | Pujos-Guillot, Estelle | Comte, Blandine | de Miranda, Joao Luis | Spiwok, Vojtech | Chorbev, Ivan | Castiglione, Filippo | Tieri, Paolo | Watterson, Steven | Mcallister, Roisin | de Melo Malaquias, Tiago | Zanin, Massimiliano | Rai, Taranjit Singh | Zheng, Huiru | University of Connecticut (UCONN) | Plateforme Exploration du Métabolisme (PFEM) ; MetaboHUB-Clermont ; MetaboHUB-MetaboHUB-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA) | Unité de Nutrition Humaine (UNH) ; Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Department of Biochemistry and Microbiology ; Institute of Chemical Technology [Prague] (ICT) | Politecnico di Milano [Milan] (POLIMI) | IAC Institute for Applied Computing | University of Ulster | ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011)
International audience
Show more [+] Less [-]English. 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.
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