期刊
TRENDS IN MOLECULAR MEDICINE
卷 27, 期 8, 页码 762-776出版社
CELL PRESS
DOI: 10.1016/j.molmed.2021.01.007
关键词
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资金
- March of Dimes Prematurity Research Center at Stanford University
- Bill and Melinda Gates Foundation [OPP1112382, OPP1189911, OPP1113682]
- National Institutes of Health [R01AG058417, R35 GM138353]
- Burroughs Wellcome Fund [1019816]
- Christopher Hess Research Fund
- Robertson Foundation
- Bill and Melinda Gates Foundation [OPP1189911, OPP1112382] Funding Source: Bill and Melinda Gates Foundation
By integrating multiomics biological data with clinical and social data using machine-learning methods, a deeper understanding of normal and abnormal pregnancies can be achieved, enabling the prediction of health trajectories for mother and offspring, as well as the development of treatment methods.
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.
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