Subject: deep learning (DL); systems medicine (SM); data integration; biomarker discovery; disease classification
Year: 2021
Type: Journal Article
Title: Deep learning in systems medicine
Author: Wang, Haiying
Author: Pujos-Guillot, Estelle
Author: Comte, Blandine
Author: de Miranda, Joao Luis
Author: Spiwok, Vojtech
Author: Chorbev, Ivan
Author: Castiglione, Filippo
Author: Tieri, Paolo
Author: Watterson, Steven
Author: McAllister, Roisin
Author: de Melo Malaquias, Tiago
Author: Zanin, Massimiliano
Author: Singh Rai, Taranjit
Author: Zheng, Huiru
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.
Publisher: Oxford University Press
Relation: Briefings in Bioinformatics
Identifier: oai:repository.ukim.mk:20.500.12188/24498
Identifier: http://hdl.handle.net/20.500.12188/24498
