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

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dc.abstract.enControlled 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 data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals’ actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorJakóbkiewicz-Banecka, Joanna
dc.contributor.authorKloska, Anna
dc.contributor.authorDrzyzga, Damian
dc.contributor.authorDrzewiecka, Dominika
dc.contributor.authorZieleń, Marcin
dc.contributor.authorWygocki, Piotr
dc.contributor.authorKotlarz, Marta
dc.contributor.authorMickiewicz, Małgorzata
dc.contributor.authorPukszta, Sebastian
dc.contributor.authorZieliński, Krystian
dc.date.accessioned2024-01-25T16:35:12Z
dc.date.available2024-01-25T16:35:12Z
dc.date.copyright2023-04-27
dc.date.issued2023
dc.description.accesstimeAT_PUBLICATION
dc.description.financePublikacja bezkosztowa
dc.description.number4
dc.description.versionFINAL_PUBLISHED
dc.description.volume19
dc.identifier.doi10.1371/JOURNAL.PCBI.1011020
dc.identifier.issn1553-734X
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/115733
dc.identifier.weblinkhttps://dx.plos.org/10.1371/journal.pcbi.1011020
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.relation.ispartofPLoS Computational Biology
dc.relation.pagese1011020: 1-18
dc.rightsCC-BY
dc.sciencecloudnosend
dc.titlePersonalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
dc.typeJournalArticle
dspace.entity.typePublication