AUTHOR=Kharb Simmi , Joshi Anagha 

TITLE=Multi-omics and machine learning for the prevention and management of female reproductive health

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1081667

DOI=10.3389/fendo.2023.1081667

ISSN=1664-2392

ABSTRACT=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.