Predicting Number of Mature Oocytes at Retrieval: A Machine-Learning Model for Patient Counseling

Abstract

metabolic processes and transcription from RNA polymerase type II promoter. The main molecular functions enriched were the protein, enhancer, chromatin, and transcription factor binding. In particular, the transcription factors ZF5, E2F, and Kaiso showed the highest statistical significance.CONCLUSIONS: IVF modifies the DNA methylation signature in the adult liver, resulting in the hypomethylation of genes involved in metabolism and regulation of gene transcription. These findings may shed light into the mechanisms underlying the developmental origin of health and disease.SUPPORT: R01HD092267 to PFR and UC MEXUS-CONACYT to SLA.

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

“…Indeed, the sequence variants directly correlating with the number of MII oocytes did not increase the model’s effectiveness. Rather than creating a model based on single variants, our approach focused on combinations of variants that were implemented in the model as genetic features and improved the prediction metrics compared to previous studies [ 40 ]. Interestingly, the average effect of genetic features on the prediction is higher than the effect of IVF protocol choice or PCOS presence.…”

“…The strength of our study lies in the number of methods used to select sequence variants associated with the number of MII oocytes and the use of multi-variant genetic features instead of single variants for modeling which increased the predictive potential of genetic data. In contrast to others [ 10 , 40 ], our approach was also strengthened by including data on retrospective stimulations. However, our study is limited by the lack of data on expression levels or structure-function predictions for variant proteins.…”