Data-driven modeling of pregnancy-related complications - Espinosa, Camilo (Trends Mol Med, 2021)
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 prediction of short- and long-term health trajectories for a mother and offspring, and 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 etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest best approaches to address disparities in outcomes affecting vulnerable populations.