Barbara Obermayer-Pietsch

Abstract Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by a multitude of factors. These include the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets.

1. Introduction Polycystic ovary syndrome (PCOS) is the most prevalent endocrine-metabolic disorder among women of reproductive age, affecting approximately 10% of women globally, with a prevalence ranging from 8 to 18% across different populations (Lüll et al., 2020). The Andean region of Latin America exhibits the highest worldwide incidence rates (Liu et al., 2021).