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The Role of Machine Learning in Decoding the 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 fertilisation rates, approximately 50% of bovine pregnancies are lost, pinpointing the need to keep studying the biological principles leading to a successful pregnancy. The increasing access and generation of omics data have aided in defining the molecular characteristics of pregnancy, i.e., embryo and foetal development and communication with the maternal environment. Large datasets generated through omics technologies are usually analysed through pipelines that could lack the power to deeply explore the complexity of biological data. Machine Learning (ML), on the other hand, 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 unravelling 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.
RD24141 Accepted 23 August 2024
© CSIRO 2024
