Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

ABSTRACT: Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical da

SUBMITTER: Zielinski K 

PROVIDER: S-EPMC10138216 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature