Data-Driven Modeling of Pregnancy-Related Complications

Area of Science:

• Reproductive biology
• Genomics
• Computational biology

Background:

• Pregnancy involves intricate biological processes like placentation, immune adaptation, and hormonal balance.
• High-throughput technologies generate multiomics, clinical, and social data for pregnancy research.
• Understanding normal and abnormal pregnancy requires integrating diverse biological and clinical information.

Purpose of the Study:

• To review advanced machine learning (ML) methods for pregnancy research.
• To explore ML's potential in uncovering novel biological insights into pregnancy.
• To identify ML applications for clarifying pregnancy pathologies and addressing health disparities.

Main Methods:

• Review of advanced machine learning techniques.
• Integration of multiomics, clinical, and social datasets.
• Application of computational methods to heterogeneous biological data.

Main Results:

• ML enables deeper understanding of normal and abnormal pregnancy.
• ML facilitates prediction of maternal and offspring health trajectories.
• ML aids in developing targeted treatments for pregnancy complications.

Conclusions:

• Advanced ML methods are crucial for deciphering complex pregnancy biology.
• Integrating multiomics and clinical data with ML can improve pregnancy outcomes.
• ML offers a powerful framework for personalized prenatal care and addressing health inequities.