Deep learning in systems medicine
Journal
Briefings in Bioinformatics
Date Issued
2021-03
Author(s)
Wang, Haiying
Pujos-Guillot, Estelle
Comte, Blandine
de Miranda, Joao Luis
Spiwok, Vojtech
Castiglione, Filippo
Tieri, Paolo
Watterson, Steven
McAllister, Roisin
de Melo Malaquias, Tiago
Zanin, Massimiliano
Singh Rai, Taranjit
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.
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.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
bbaa237.pdf
Size
911.77 KB
Format
Adobe PDF
Checksum
(MD5):ba396729ef85c1e94c6732967a6ec938