A combination of immune cell types identified through ensemble machine learning strategy detects altered profile in recurrent pregnancy loss: a pilot study.

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Bibliographic Details
Title: A combination of immune cell types identified through ensemble machine learning strategy detects altered profile in recurrent pregnancy loss: a pilot study.
Authors: Benner M; Radboud Institute of Molecular Life Sciences, Department of Laboratory Medicine, Laboratory of Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands., Feyaerts D; Radboud Institute of Molecular Life Sciences, Department of Laboratory Medicine, Laboratory of Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands., Lopez-Rincon A; Department of Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands., van der Heijden OWH; Department of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, the Netherlands., van der Hoorn ML; Department of Gynecology and Obstetrics, Leiden University Medical Center, Leiden, the Netherlands., Joosten I; Radboud Institute of Molecular Life Sciences, Department of Laboratory Medicine, Laboratory of Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands., Ferwerda G; Radboud Institute of Molecular Life Sciences, Department of Laboratory Medicine, Laboratory of Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands., van der Molen RG; Radboud Institute of Molecular Life Sciences, Department of Laboratory Medicine, Laboratory of Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: Renate.vanderMolen@radboudumc.nl.
Source: F&S science [F S Sci] 2022 May; Vol. 3 (2), pp. 166-173. Date of Electronic Publication: 2022 Feb 09.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101765857 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2666-335X (Electronic) Linking ISSN: 2666335X NLM ISO Abbreviation: F S Sci Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2666-335X
DOI:10.1016/j.xfss.2022.02.002