Wyszukiwarka
Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
Abstrakt
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.
Cytowania
-
2
CrossRef
-
0
Web of Science
-
2
Scopus
Autorzy (10)
-
Sebastian Pukszta dr
-
Małgorzata Mickiewicz dr
-
Marta Kotlarz mgr
-
Piotr Wygocki dr
-
Marcin Zieleń mgr
-
Dominika Drzewiecka mgr
-
Damian Drzyzga dr
-
Anna Kloska dr
-
Joanna Jakóbkiewicz-Banecka dr hab.
Cytuj jako
Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1371/journal.pcbi.1011020
- Licencja
-
otwiera się w nowej karcie
Słowa kluczowe
Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
-
PLoS Computational Biology
nr 19,
strony 1 - 18,
ISSN: 1553-7358 - Język:
- angielski
- Rok wydania:
- 2023
- Opis bibliograficzny:
- Zieliński K., Pukszta S., Mickiewicz M., Kotlarz M., Wygocki P., Zieleń M., Drzewiecka D., Drzyzga D., Kloska A., Jakóbkiewicz-Banecka J.: Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data// PLoS Computational Biology -Vol. 19,iss. 4 (2023), s.1-18
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1371/journal.pcbi.1011020
- Źródła finansowania:
-
- COST_FREE
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 26 razy
Publikacje, które mogą cię zainteresować
Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
- K. Zieliński,
- S. Pukszta,
- M. Mickiewicz
- + 7 autorów
Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
- M. A. Jopek,
- K. Pastuszak,
- S. Cygert
- + 5 autorów
A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
- K. Saboo,
- Y. Varatharajah,
- B. M. Berry
- + 11 autorów
Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires
- K. Zieliński,
- D. Drabczyk,
- M. Kunicki
- + 3 autorów