{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T09:35:07Z","timestamp":1768988107932,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.5\u2014Call for tender No. 3277 of 30\/12\/2021 of Italian Ministry of University and Research"},{"name":"the European Union\u2014NextGenerationEU"},{"name":"Fondazione Cariparma as part of the Parma Microbiota project and \u2018Characterization of the Metabolic Potential of the Human Microbiota in European Populations\u2019 project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Recent studies have shown correlations between the microbiota\u2019s composition and various health conditions. Machine learning (ML) techniques are essential for analyzing complex biological data, particularly in microbiome research. ML methods help analyze large datasets to uncover microbiota patterns and understand how these patterns affect human health. This study introduces a novel approach combining statistical physics with the Monte Carlo (MC) methods to characterize bacterial species in the human microbiota. We assess the significance of bacterial species in different age groups by using notions of statistical distances to evaluate species prevalence and abundance across age groups and employing MC simulations based on statistical mechanics principles. Our findings show that the microbiota composition experiences a significant transition from early childhood to adulthood. Species such as Bifidobacterium breve and Veillonella parvula decrease with age, while others like Agathobaculum butyriciproducens and Eubacterium rectale increase. Additionally, low-prevalence species may hold significant importance in characterizing age groups. Finally, we propose an overall species ranking by integrating the methods proposed here in a multicriteria classification strategy. Our research provides a comprehensive tool for microbiota analysis using statistical notions, ML techniques, and MC simulations.<\/jats:p>","DOI":"10.3390\/make6040117","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T07:53:43Z","timestamp":1729583623000},"page":"2375-2399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Monte Carlo Approaches and Statistical Physics Notions to Characterize Bacterial Species in Human Microbiota"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5729-1123","authenticated-orcid":false,"given":"Michele","family":"Bellingeri","sequence":"first","affiliation":[{"name":"Dipartimento di Scienze Matematiche, Fisiche e Informatiche, University of Parma, Via G.P. Usberti, 7\/a, 43124 Parma, Italy"},{"name":"Istituto Nazione di Fisica Nucleare, INFN, Gruppo Collegato di Parma, 43124 Parma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1744-2214","authenticated-orcid":false,"given":"Leonardo","family":"Mancabelli","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, University of Parma, 43124 Parma, Italy"},{"name":"Interdepartmental Research Centre \u201cMicrobiome Research Hub\u201d, University of Parma, 43124 Parma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-3164","authenticated-orcid":false,"given":"Christian","family":"Milani","sequence":"additional","affiliation":[{"name":"Interdepartmental Research Centre \u201cMicrobiome Research Hub\u201d, University of Parma, 43124 Parma, Italy"},{"name":"Laboratory of Probiogenomics, Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3024-0537","authenticated-orcid":false,"given":"Gabriele Andrea","family":"Lugli","sequence":"additional","affiliation":[{"name":"Interdepartmental Research Centre \u201cMicrobiome Research Hub\u201d, University of Parma, 43124 Parma, Italy"},{"name":"Laboratory of Probiogenomics, Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy"}]},{"given":"Roberto","family":"Alfieri","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Matematiche, Fisiche e Informatiche, University of Parma, Via G.P. Usberti, 7\/a, 43124 Parma, Italy"},{"name":"Istituto Nazione di Fisica Nucleare, INFN, Gruppo Collegato di Parma, 43124 Parma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6419-1075","authenticated-orcid":false,"given":"Massimiliano","family":"Turchetto","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Matematiche, Fisiche e Informatiche, University of Parma, Via G.P. Usberti, 7\/a, 43124 Parma, Italy"},{"name":"Istituto Nazione di Fisica Nucleare, INFN, Gruppo Collegato di Parma, 43124 Parma, Italy"}]},{"given":"Marco","family":"Ventura","sequence":"additional","affiliation":[{"name":"Interdepartmental Research Centre \u201cMicrobiome Research Hub\u201d, University of Parma, 43124 Parma, Italy"},{"name":"Laboratory of Probiogenomics, Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy"}]},{"given":"Davide","family":"Cassi","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Matematiche, Fisiche e Informatiche, University of Parma, Via G.P. Usberti, 7\/a, 43124 Parma, Italy"},{"name":"Istituto Nazione di Fisica Nucleare, INFN, Gruppo Collegato di Parma, 43124 Parma, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, C., and Jackson, S.A. (2019). 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