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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011020 |
| _version_ | 1797831717392744448 |
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| author | Krystian Zieliński Sebastian Pukszta Małgorzata Mickiewicz Marta Kotlarz Piotr Wygocki Marcin Zieleń Dominika Drzewiecka Damian Drzyzga Anna Kloska Joanna Jakóbkiewicz-Banecka |
| author_facet | Krystian Zieliński Sebastian Pukszta Małgorzata Mickiewicz Marta Kotlarz Piotr Wygocki Marcin Zieleń Dominika Drzewiecka Damian Drzyzga Anna Kloska Joanna Jakóbkiewicz-Banecka |
| author_sort | Krystian Zieliński |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T13:56:11Z |
| format | Article |
| id | doaj.art-e799bc8782454700a033c542d4058dd7 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-09T13:56:11Z |
| publishDate | 2023-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e799bc8782454700a033c542d4058dd72023-05-08T05:31:12ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-04-01194e101102010.1371/journal.pcbi.1011020Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.Krystian ZielińskiSebastian PuksztaMałgorzata MickiewiczMarta KotlarzPiotr WygockiMarcin ZieleńDominika DrzewieckaDamian DrzyzgaAnna KloskaJoanna Jakóbkiewicz-BaneckaControlled 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.https://doi.org/10.1371/journal.pcbi.1011020 |
| spellingShingle | Krystian Zieliński Sebastian Pukszta Małgorzata Mickiewicz Marta Kotlarz Piotr Wygocki Marcin Zieleń Dominika Drzewiecka Damian Drzyzga Anna Kloska Joanna Jakóbkiewicz-Banecka Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1011020 |
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