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

doi:10.1093/bib/bbaa237

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 journal › Review article › peer-review

12 Citations (Scopus)

160 Downloads (Pure)

Abstract

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 language	English
Pages (from-to)	1543-1559
Number of pages	17
Journal	Briefings in Bioinformatics
Volume	22
Issue number	2
Early online date	16 Nov 2020
DOIs	https://doi.org/10.1093/bib/bbaa237
Publication status	Published (in print/issue) - 1 Mar 2021

Bibliographical note

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

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/bib/bbaa237

bbaa237Final published version, 912 KBLicence: CC BY

Cite this

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abstract = "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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AU - Chorbev, Ivan

AU - Castiglione, Filippo

AU - Tieri, Paolo

AU - Watterson, Steven

AU - Mc Allister, Roisin

AU - De Melo Malaquias, Tiago

AU - Zanin, Massimiliano

AU - Rai, Taranjit Singh

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Deep Learning in Systems Medicine

Abstract

Bibliographical note

Keywords

UN SDGs

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