1. Data-Driven Modeling of Pregnancy-Related Complications
- Author
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Maria Xenochristou, Martin Becker, David K. Stevenson, Martin S. Angst, Thanaphong Phongpreecha, Gary M. Shaw, Alan L. Chang, Nima Aghaeepour, Brice Gaudilliere, Ina A. Stelzer, Camilo Espinosa, Ronald J. Wong, Ivana Maric, Yair J. Blumenfeld, Laura S. Peterson, Michael Katz, and Davide De Francesco
- Subjects
Proteomics ,0301 basic medicine ,medicine.medical_specialty ,Offspring ,Systems biology ,Models, Biological ,Risk Assessment ,Article ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Pregnancy ,Risk Factors ,Data Mining ,Humans ,Metabolomics ,Medicine ,Maternal health ,Intensive care medicine ,Molecular Biology ,Biological data ,Reproductive Physiological Phenomena ,business.industry ,Pregnancy Outcome ,Computational Biology ,Placentation ,Genomics ,medicine.disease ,Pregnancy Complications ,030104 developmental biology ,Molecular Medicine ,Female ,Disease Susceptibility ,business ,Biomarkers ,030217 neurology & neurosurgery - Abstract
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.
- Published
- 2021
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