Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts.

Autor:	Vänni P; Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland., Tejesvi MV; Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland.; Ecology and Genetics, Faculty of Science, University of Oulu, Oulu, Finland., Paalanne N; Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland.; Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, University of Oulu, Oulu, Finland., Aagaard K; Department of Obstetrics & Gynecology, Division of Maternal-Fetal Medicine, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA., Ackermann G; Department of Pediatrics, University of California, San Diego, California, USA., Camargo CA Jr; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Eggesbø M; Department of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway., Hasegawa K; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Hoen AG; Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA., Karagas MR; Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA., Kolho K-L; Children's Hospital, University of Helsinki and HUS, Helsinki, Finland., Laursen MF; National Food Institute, Technical University of Denmark, Lyngby, Denmark., Ludvigsson J; Crown Princess Victoria Children's Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden., Madan J; Department of Psychiatry, Dartmouth Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.; Department of Pediatrics, Dartmouth Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA., Ownby D; Medical College of Georgia, Augusta, Georgia, USA., Stanton C; Teagasc Food Research Centre & APC Microbiome Ireland, Moorepark, Fermoy, Co. Cork, Ireland., Stokholm J; Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.; Department of Food Science, University of Copenhagen, Copenhagen, Denmark., Tapiainen T; Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland.; Department of Obstetrics & Gynecology, Division of Maternal-Fetal Medicine, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.; Biocenter Oulu, University of Oulu, Oulu, Finland.
Jazyk:	angličtina
Zdroj:	MSystems [mSystems] 2023 Dec 21; Vol. 8 (6), pp. e0036423. Date of Electronic Publication: 2023 Oct 24.
DOI:	10.1128/msystems.00364-23
Abstrakt:	Importance: There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations. Competing Interests: The authors declare no conflict of interest.
Databáze:	MEDLINE
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