Data-Driven Modeling of Pregnancy-Related Complications.
Biomarkers
Computational Biology
/ methods
Data Mining
Disease Susceptibility
Female
Genomics
/ methods
Humans
Machine Learning
Metabolomics
/ methods
Models, Biological
Pregnancy
Pregnancy Complications
/ diagnosis
Pregnancy Outcome
Proteomics
/ methods
Reproductive Physiological Phenomena
Risk Assessment
Risk Factors
machine learning
maternal health
multimodal learning
multiomics
multitask learning
pregnancy
systems biology
Journal
Trends in molecular medicine
ISSN: 1471-499X
Titre abrégé: Trends Mol Med
Pays: England
ID NLM: 100966035
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
received:
09
10
2020
revised:
01
12
2020
accepted:
20
01
2021
pubmed:
13
2
2021
medline:
5
1
2022
entrez:
12
2
2021
Statut:
ppublish
Résumé
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
Identifiants
pubmed: 33573911
pii: S1471-4914(21)00039-3
doi: 10.1016/j.molmed.2021.01.007
pmc: PMC8324504
mid: NIHMS1667121
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
762-776Subventions
Organisme : NIA NIH HHS
ID : R01 AG058417
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM138353
Pays : United States
Investigateurs
Ina A Stelzer
(IA)
Laura S Peterson
(LS)
Alan L Chang
(AL)
Maria Xenochristou
(M)
Thanaphong Phongpreecha
(T)
Davide De Francesco
(D)
Michael Katz
(M)
Yair J Blumenfeld
(YJ)
Martin S Angst
(MS)
Informations de copyright
Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Interests No interests are declared.
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