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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10138216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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