Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions

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dc.contributor.authorMoreno-Indias, Isabel
dc.contributor.authorLahti, Leo
dc.contributor.authorNedyalkova, Miroslava
dc.contributor.authorElbere, Ilze
dc.contributor.authorRoshchupkin, Gennady
dc.contributor.authorAdilovic, Muhamed
dc.contributor.authorAydemir, Onder
dc.contributor.authorBakir-Gungor, Burcu
dc.contributor.authorPau, Enrique Carrillo De Santa
dc.contributor.authorD'Elia, Domenica
dc.contributor.authorDesai, Mahesh
dc.contributor.authorFalquet, Laurent
dc.contributor.authorGundogdu, Aycan
dc.contributor.authorHron, Karel
dc.contributor.authorKlammsteiner, Thomas
dc.contributor.authorLopes, Marta B.
dc.contributor.authorMarcos-Zambrano, Laura Judith
dc.contributor.authorMarques, Cláudia
dc.contributor.authorMason, Michael
dc.contributor.authorMay, Patrick
dc.contributor.authorPasic, Lejla
dc.contributor.authorPio, Gianvito
dc.contributor.authorPongor, Sándor
dc.contributor.authorPromponas, Vasilis J.
dc.contributor.authorPrzymus, Piotr
dc.contributor.authorSaez-Rodriguez, Julio
dc.contributor.authorSampri, Alexia
dc.contributor.authorShigdel, Rajesh
dc.contributor.authorStres, Blaz
dc.contributor.authorSuharoschi, Ramona
dc.contributor.authorTruu, Jaak
dc.contributor.authorTruica, Ciprian-Octavian
dc.contributor.authorVilne, Baiba
dc.contributor.authorVlachakis, Dimitrios P.
dc.contributor.authorYilmaz, Ercüment
dc.contributor.authorZeller, Georg
dc.contributor.authorZomer, Aldert
dc.contributor.authorGomez-Cabrero, David
dc.contributor.authorClaesson, Marcus J.
dc.contributor.funderInstituto de Salud Carlos IIIen
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderHorizon 2020en
dc.contributor.funderFonds National de la Recherche Luxembourgen
dc.contributor.funderEuropean Cooperation in Science and Technologyen
dc.date.accessioned2022-11-10T12:33:15Z
dc.date.available2022-11-10T12:33:15Z
dc.date.issued2021-02
dc.date.updated2022-11-10T11:32:44Z
dc.description.abstractThe human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.en
dc.description.sponsorshipEuropean Cooperation in Science and Technology (COST Action CA18131); Luxembourg National Research Fund (FNR) CORE (Grant C18/BM/12585940)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid635781en
dc.identifier.citationMoreno-Indias, I., Lahti, L., Nedyalkova, M., Elbere, I., Roshchupkin, G., Adilovic, M., Aydemir, O., Bakir-Gungor, B., Pau, E. C. D. S., D'Elia, D., Desai, M., Falquet, L., Gundogdu, A., Hron, K., Klammsteiner, T., Lopes, M. B., Marcos-Zambrano, L. J., Marques, C., Mason, M., May, P., Pasic, L., Pio, G., Pongor, S., Promponas, V. J., Przymus, P., Saez-Rodriguez, J., Sampri, A., Shigdel, R., Stres, B., Suharoschi, R., Truu, J., Truica, C-O., Vilne, B., Vlachakis, D. P., Yilmaz, E., Zeller, G., Zomer, A., Gomez-Cabrero, D. and Claesson, M. J. (2021) 'Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions', Frontiers In Microbiology, 12, 635781, (9pp). doi: 10.3389/fmicb.2021.635781en
dc.identifier.doi10.3389/fmicb.2021.635781en
dc.identifier.endpage9en
dc.identifier.issn1664-302X
dc.identifier.journaltitleFrontiers In Microbiologyen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13846
dc.identifier.volume12en
dc.language.isoenen
dc.publisherFrontiers Media S.A.en
dc.relation.projectinfo:eu-repo/grantAgreement/AKA//295741/FI/Ecological modeling of the human gut microbiome: individuality, dynamics, and function in large population cohorts/en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::CSA/857572/EU/Integration of knowledge and biobank resources in comprehensive translational approach for personalized prevention and treatment of metabolic disorders/INTEGROMEDen
dc.rights© 2021 Moreno-Indias, Lahti, Nedyalkova, Elbere, Roshchupkin, Adilovic, Aydemir, Bakir-Gungor, Santa Pau, D’Elia, Desai, Falquet, Gundogdu, Hron, Klammsteiner, Lopes,Marcos-Zambrano,Marques,Mason,May, Paši´c, Pio, Pongor, Promponas, Przymus, Saez-Rodriguez, Sampri, Shigdel, Stres, Suharoschi, Truu, Truic˘a, Vilne, Vlachakis, Yilmaz, Zeller, Zomer, Gómez-Cabrero and Claesson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMachine learningen
dc.subjectMicrobiomeen
dc.subjectML4Microbiomeen
dc.subjectPersonalized medicineen
dc.subjectBiomarker identificationen
dc.titleStatistical and machine learning techniques in human microbiome studies: contemporary challenges and solutionsen
dc.typeArticle (peer-reviewed)en
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