TY - JOUR
T1 - Deep Learning in Systems Medicine
AU - Wang, Haiying / HY
AU - Pujos-Guillot, Estelle
AU - Comte, Blandine
AU - Luis de Miranda, Joao
AU - Vojtech Spiwok
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
AU - Zheng, Huiru
PY - 2021/3/31
Y1 - 2021/3/31
N2 - Systems Medicine 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 holds great promise in this endeavour. This review paper addressed the main developments of Deep Learning algorithms and a set of general topics where Deep Learning is decisive; namely, within the Systems Medicine landscape. It discusses how Deep Learning can be applied to Systems Medicine 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 Deep Learning in Systems Medicine, one of them is involving the creation of a model for personalised Parkinson's disease. The review offers valuable insights and informs the research in Deep Learning and Systems Medicine.
AB - Systems Medicine 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 holds great promise in this endeavour. This review paper addressed the main developments of Deep Learning algorithms and a set of general topics where Deep Learning is decisive; namely, within the Systems Medicine landscape. It discusses how Deep Learning can be applied to Systems Medicine 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 Deep Learning in Systems Medicine, one of them is involving the creation of a model for personalised Parkinson's disease. The review offers valuable insights and informs the research in Deep Learning and Systems Medicine.
KW - Deep Learning (DL)
KW - Systems Medicine (SM)
KW - Data integration
KW - Biomarker discovery
KW - disease classification
U2 - 10.1093/bib/bbaa237
DO - 10.1093/bib/bbaa237
M3 - Article
C2 - 33197934
VL - 22
SP - 1543
EP - 1559
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
SN - 1467-5463
IS - 2
ER -
