[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118248-en":3,"doc-seo-118248-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118248,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Gut Microbiome Insights for Pediatric Health Using Machine Learning Analyses","The human gut microbiome shifts markedly during the perinatal period under influences including delivery mode, exposure to antibiotics, and breastfeeding. This thesis applies machine learning to address the complexity, multidimensionality, noise, and sparsity of microbiota-derived data in pediatric cohorts. Machine learning models are used in within-study and cross-study analyses across three studies, including delivery mode and perinatal antibiotics, gut mycobiome associations with non-communicable outcomes, and generalization across diverse infant cohorts. Results show altered infant gut microbiota after cesarean delivery and perinatal antibiotics, with diminished Bacteroides and related metabolic pathways, and cross-study validation of delivery-related microbial patterns.","OULU 2024  \nD 1793  \nUNIVERSITAT IS  \nOULU ENSIS  \nD  \nMEDICA  \nPetri Vänni  \nGUT MICROBIOME INSIGHTS FOR PEDIATRIC HEALTH USING MACHINE LEARNING ANALYSES  \nUNIVERSITY OF OULU GRADUATE SCHOOL;  \nUNIVERSITY OF OULU, FACULTY OF MEDICINE  \nACTA  \nA C T A U N I V E R S I T A T I S O U L U E N S I SD Medic a 1793  \nPETRI VÄNNI  \nGUT MICROBIOME INSIGHTS FOR PEDIATRIC HEALTH USING MACHINE LEARNING ANALYSES  \nAcademic dissertation to be presented with the assent of the Doctoral Programme Committee of Health and Biosciences of the University of Oulu for public defence in Auditorium 12 of Oulu University Hospital (Kajaanintie 50), on 14 June 2024, at 12 noon  \nUNIVERSITY OF OULU, OULU 2024  \nCopyright © 2024  \nActa Univ. Oul. D 1793, 2024  \nSupervised by  \nProfessor Terhi Ruuska Docent Tejesvi Mysore  \nReviewed by  \nAssociate Professor Samuli Rautava Docent Antti Karkman  \nOpponent  \nDocent Tommi Vatanen  \nISBN 978-952-62-4121-0 (Paperback)  \nISBN 978-952-62-4122-7 (PDF)  \nISSN 0355-3221 (Printed)  \nISSN 1796-2234 (Online)  \nCover Design Raimo Ahonen  \nPUNAMUSTA TAMPERE 2024  \nVänni, Petri, Gut microbiome insights for pediatric health using machine learning analyses  \nUniversity of Oulu Graduate School; University of Oulu, Faculty of Medicine Acta Univ. Oul. D 1793, 2024  \nUniversity of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland  \nAbstract  \nThe human gut microbiome, influenced by factors such as delivery mode, exposure to antibiotics, and breastfeeding, undergoes significant alterations during the perinatal period. This study investigated the use of machine learning methods to manage the complexity, multidimensionality, noise, and sparsity of microbiota-derived data in the data analysis in pediatric cohorts. Machine learning methods were employed in both within-study and crossstudy settings.  \nThis thesis comprises three studies. The first study investigated the impact of delivery mode and perinatal antibiotics on the developing infant gut microbiota. The second study examined the relationship between gut mycobiome and non-communicable diseases. In the third study, the relationship between delivery mode and infant gut microbiota was further investigated in diverse infant cohorts from several countries. In all the studies, various machine learning methods were utilized for sample classification.  \nThe first study revealed altered gut microbiota profiles in infants delivered via cesarean section or exposed to perinatal antibiotics. Bacteroides and the associated metabolic pathways were significantly diminished in such infants. The second study revealed weak associations between early-life gut mycobiome composition and subsequent atopic dermatitis and overweight. In the third study, which used a cross-study setting, the machine learning models demonstrated that the microbiota changes related to cesarean section were generalizable across cohorts when a model developed and tested in one infant cohort was used and validated in other cohorts. Based on gut microbiota data, these cross-study analyses identified the relative abundance of Bacteroides as a crucial factor in classifying the mode of delivery.  \nMachine learning methods can be effectively used in pediatric microbiome research. Furthermore, validating and generalizing the microbial patterns between different clinical pediatric cohorts and datasets could be a useful strategy while utilizing machine learning models.  \nKeywords: 16S rRNA, antibiotics, atopic dermatitis, bacteria, bacteriome, bioinformatics, cesarean section, children, classification, classifier, delivery mode, decision tree, ensemble, fungi, infants, ITS, machine learning, microbiome, model, mycobiome, neural network, obesity, prospective cohort, random forest, vaginal  \nVänni, Petri, Lasten suolistomikrobiston tutkiminen koneoppimismenetelmin  \nOulun yliopiston tutkijakoulu; Oulun yliopisto, lääketieteellinen tiedekunta Acta Univ. Oul. D 1793, 2024  \nOulun yliopisto, PL 8000, 90014 Oulun yliopisto  \nTiivi","cbCaitTKYoBZe7YT","https://ap.wps.com/l/cbCaitTKYoBZe7YT","pdf",2592757,1,138,"English","en",105,"# Abstract\n## Perinatal microbiome changes and study rationale\n## Machine learning approach in pediatric cohorts\n## Key findings across three studies\n## Implications for model validation and generalization","[{\"question\":\"Which perinatal factors were investigated for their impact on the infant gut microbiome?\",\"answer\":\"The thesis examines delivery mode, exposure to perinatal antibiotics, and breastfeeding-related influences during the perinatal period.\"},{\"question\":\"How were machine learning methods applied in the pediatric studies?\",\"answer\":\"Machine learning methods were used for sample classification in both within-study and cross-study settings to manage data complexity and identify important variables.\"},{\"question\":\"What did the cross-study analyses show about cesarean-section-related microbiota changes?\",\"answer\":\"Models trained and validated across cohorts indicated that cesarean-section-associated microbiota changes were generalizable, with the relative abundance of Bacteroides identified as a crucial classification factor.\"}]","Gut Microbiome Insights for Pediatric Health Using Machine Learning Analyses | 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perinatal factors were investigated for their impact on the infant gut microbiome?","Question",{"text":75,"@type":76},"The thesis examines delivery mode, exposure to perinatal antibiotics, and breastfeeding-related influences during the perinatal period.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were machine learning methods applied in the pediatric studies?",{"text":80,"@type":76},"Machine learning methods were used for sample classification in both within-study and cross-study settings to manage data complexity and identify important variables.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the cross-study analyses show about cesarean-section-related microbiota changes?",{"text":84,"@type":76},"Models trained and validated across cohorts indicated that cesarean-section-associated microbiota changes were generalizable, with the relative abundance of Bacteroides identified as a crucial classification 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