Multi-omics and machine learning for the prevention and management of female reproductive health - Kharb, Simmi; Joshi, Anagha (Frontiers in Endocrinology, 2023)
Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at great cost of women’s reproductive health. Pregnancy thus became a highly demanding phase in woman’s life cycle both physically and emotionally and therefore needs monitoring to assure optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to increasing maternal age and global obesity pandemic, demand closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explore utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, metabolomics) towards diagnosis, prognosis and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complimentary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g. omics, wearables) have shown a promise towards diagnosis, prognosis and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.