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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| Title: | 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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| 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 |
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