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

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 predi...

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Autores principales: Zieliński, Krystian, Pukszta, Sebastian, Mickiewicz, Małgorzata, Kotlarz, Marta, Wygocki, Piotr, Zieleń, Marcin, Drzewiecka, Dominika, Drzyzga, Damian, Kloska, Anna, Jakóbkiewicz-Banecka, Joanna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138216/
https://www.ncbi.nlm.nih.gov/pubmed/37104276
http://dx.doi.org/10.1371/journal.pcbi.1011020
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author Zieliński, Krystian
Pukszta, Sebastian
Mickiewicz, Małgorzata
Kotlarz, Marta
Wygocki, Piotr
Zieleń, Marcin
Drzewiecka, Dominika
Drzyzga, Damian
Kloska, Anna
Jakóbkiewicz-Banecka, Joanna
author_facet Zieliński, Krystian
Pukszta, Sebastian
Mickiewicz, Małgorzata
Kotlarz, Marta
Wygocki, Piotr
Zieleń, Marcin
Drzewiecka, Dominika
Drzyzga, Damian
Kloska, Anna
Jakóbkiewicz-Banecka, Joanna
author_sort Zieliński, Krystian
collection PubMed
description 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 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.
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spelling pubmed-101382162023-04-28 Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data Zieliński, Krystian Pukszta, Sebastian Mickiewicz, Małgorzata Kotlarz, Marta Wygocki, Piotr Zieleń, Marcin Drzewiecka, Dominika Drzyzga, Damian Kloska, Anna Jakóbkiewicz-Banecka, Joanna PLoS Comput Biol Research Article 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 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. Public Library of Science 2023-04-27 /pmc/articles/PMC10138216/ /pubmed/37104276 http://dx.doi.org/10.1371/journal.pcbi.1011020 Text en © 2023 Zieliński et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zieliński, Krystian
Pukszta, Sebastian
Mickiewicz, Małgorzata
Kotlarz, Marta
Wygocki, Piotr
Zieleń, Marcin
Drzewiecka, Dominika
Drzyzga, Damian
Kloska, Anna
Jakóbkiewicz-Banecka, Joanna
Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
title Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
title_full Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
title_fullStr Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
title_full_unstemmed Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
title_short Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
title_sort personalized prediction of the secondary oocytes number after ovarian stimulation: a machine learning model based on clinical and genetic data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138216/
https://www.ncbi.nlm.nih.gov/pubmed/37104276
http://dx.doi.org/10.1371/journal.pcbi.1011020
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