{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T22:56:45Z","timestamp":1789340205186,"version":"build-2803163510"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["No.RPPM.01.01.01-22-0060\/17"],"award-info":[{"award-number":["No.RPPM.01.01.01-22-0060\/17"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>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\u2019s 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-M\u00fcllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the <jats:italic>GDF9<\/jats:italic>, <jats:italic>LHCGR<\/jats:italic>, <jats:italic>FSHB<\/jats:italic>, <jats:italic>ESR1<\/jats:italic>, and <jats:italic>ESR2<\/jats:italic> genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-M\u00fcllerian hormone. Predictions of our clinical-genetic model accurately matched individuals\u2019 actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011020","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T17:27:44Z","timestamp":1682616464000},"page":"e1011020","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":25,"title":["Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data"],"prefix":"10.1371","volume":"19","author":[{"given":"Krystian","family":"Zieli\u0144ski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Pukszta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ma\u0142gorzata","family":"Mickiewicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Kotlarz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Wygocki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Ziele\u0144","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominika","family":"Drzewiecka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damian","family":"Drzyzga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Kloska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8846-5448","authenticated-orcid":true,"given":"Joanna","family":"Jak\u00f3bkiewicz-Banecka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"pcbi.1011020.ref001","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.clinbiochem.2018.03.012","article-title":"Fertility and infertility: Definition and epidemiology","volume":"62","author":"M Vander Borght","year":"2018","journal-title":"Clin Biochem"},{"key":"pcbi.1011020.ref002","doi-asserted-by":"crossref","first-page":"1700314","DOI":"10.1183\/13993003.00314-2017","article-title":"Infertility in an adult cohort with primary ciliary dyskinesia: phenotype\u2013gene association","volume":"50","author":"GJ Vanaken","year":"2017","journal-title":"European Respiratory Journal"},{"key":"pcbi.1011020.ref003","first-page":"343","article-title":"Can Inhibin B Reflect Ovarian Reserve of Healthy Reproductive Age Women Effectively?","volume":"12","author":"J Wen","year":"2021","journal-title":"Front Endocrinol (Lausanne)."},{"key":"pcbi.1011020.ref004","article-title":"Female Infertility.","author":"MH Walker","year":"2022","journal-title":"StatPearls. 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