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Title

Deep learning in systems medicine

AuthorsWang, Haiying; Pujos-Guillot, Estelle; Comte, Blandine; Miranda, Joao Luis de; Spiwok, Vojtech; Chorbev, Ivan; Castiglione, Filippo; Tieri, Paolo; Watterson, Steven; McAllister, Roisin; Melo Malaquias, Tiago de; Zanin, Massimiliano; Rai, Taranjit Singh; Zheng, Huiru
KeywordsDeep learning (DL)
Systems medicine (SM)
Data integration
Biomarker discovery
Disease classification
Issue DateMar-2021
PublisherOxford University Press
CitationBriefings in Bioinformatics 22(2): 1543-1559 (2021)
AbstractSystems 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.
Publisher version (URL)https://doi.org/10.1093/bib/bbaa237
URIhttp://hdl.handle.net/10261/229432
DOIhttp://dx.doi.org/10.1093/bib/bbaa237
ISSN1467-5463
E-ISSN1477-4054
Appears in Collections:(IFISC) Artículos
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