The role of machine learning in decoding the molecular complexity of bovine pregnancy: a review

Abstract

Pregnancy establishment and progression in cattle are pivotal research areas with significant implications for the industry. Despite high fertilization rates, ~50% of bovine pregnancies are lost, pinpointing the need to keep studying the biological principles leading to a successful pregnancy. The increasing access to and generation of omics data have aided in defining the molecular characteristics of pregnancy, i.e. embryo and fetal development and communication with the maternal environment. Large datasets generated through omics technologies are usually analyzed through pipelines that could lack the power to deeply explore the complexity of biological data. Machine learning (ML), a branch of artificial intelligence, offers a promising approach to address this challenge by effectively handling large-scale, heterogeneous and high-dimensional data. This review explores the role of ML in unraveling the intricacies of bovine embryo–maternal communication, including the identification of biomarkers associated with pregnancy outcome prediction and uncovering key genes and pathways involved in embryo development and survival. Through discussing recent studies, we define the contributions of ML towards advancing our understanding of bovine pregnancy, with the final goal of reducing pregnancy losses and enhancing reproductive efficiency while also addressing current limitations and future perspectives of ML in this field.