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

Autor: Zieliński K; INVICTA Research and Development Center, Sopot, Poland.; Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland., Pukszta S; INVICTA Research and Development Center, Sopot, Poland., Mickiewicz M; INVICTA Research and Development Center, Sopot, Poland., Kotlarz M; INVICTA Research and Development Center, Sopot, Poland., Wygocki P; Institute of Computer Science, Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland.; MIM Solutions, Research and Development Center, Warsaw, Poland., Zieleń M; MIM Solutions, Research and Development Center, Warsaw, Poland., Drzewiecka D; INVICTA Research and Development Center, Sopot, Poland., Drzyzga D; INVICTA Research and Development Center, Sopot, Poland., Kloska A; Department of Medical Biology and Genetics, Faculty of Biology, University of Gdańsk, Gdańsk, Poland., Jakóbkiewicz-Banecka J; INVICTA Research and Development Center, Sopot, Poland.; Department of Medical Biology and Genetics, Faculty of Biology, University of Gdańsk, Gdańsk, Poland.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Apr 27; Vol. 19 (4), pp. e1011020. Date of Electronic Publication: 2023 Apr 27 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011020
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.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: KZ, SP, MM, MK, DD, DD, and JJ-B are employees of INVICTA, clinics and medical laboratories for infertility treatment. PW and MZ are employees of MIM Solutions. The affiliation does not affect the authors’ impartiality, adherence to journal standards and policies, or availability of data. AK declares no conflict of interest.
(Copyright: © 2023 Zieliński et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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