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dc.contributor.authorBakir-Gungor, Burcu
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.authorSanta Pau, Enrique Carrillo-de
dc.contributor.authorD'Elia, Domenica
dc.contributor.authorDesai, Mahesh S.
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, Claudia
dc.contributor.authorMason, Michael
dc.contributor.authorMay, Patrick
dc.contributor.authorPasic, Lejla
dc.contributor.authorPio, Gianvito
dc.contributor.authorPongor, Sandor
dc.contributor.authorPromponas, Vasilis J.
dc.contributor.authorPrzymus, Piotr
dc.contributor.authorSaez-Rodriguez, Julio
dc.contributor.authorampri, 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
dc.contributor.authorYilmaz, Ercument
dc.contributor.authorZeller, Georg
dc.contributor.authorZomer, Aldert L.
dc.contributor.authorGomez-Cabrero, David
dc.contributor.authorClaesson, Marcus J.
dc.date.accessioned2022-03-05T10:37:42Z
dc.date.available2022-03-05T10:37:42Z
dc.date.issued2021en_US
dc.identifier.issn1664-302X
dc.identifier.otherPubMed ID33692771
dc.identifier.urihttps //doi.org/10.3389/fmicb.2021.635781
dc.identifier.urihttps://hdl.handle.net/20.500.12573/1246
dc.descriptionThis study was supported by the COST Action CA18131 "Statistical and machine learning techniques in human microbiome studies." IM-I was supported by the "MS type I" program (CP16/00163) from the Instituto de Salud Carlos III and co-funded by Fondo Europeo de Desarrollo Regional-FEDER. MN was grateful for the additional support by the project "Information and Communication Technologies for a Single Digital Market in Science, Education and Security" of the Scientific Research Center, NIS-3317 and National roadmaps for research infrastructures (RIs) grant number NIS-3318. LL was supported by Academy of Finland (decision 295741). IE was supported by H2020-EU.4.b. project "Integration of knowledge and biobank resources in comprehensive translational approach for personalized prevention and treatment of metabolic disorders (INTEGROMED)" (grant agreement ID 857572). MD was supported by the Luxembourg National Research Fund (FNR) CORE grant (C18/BM/12585940).en_US
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_US
dc.description.sponsorshipEuropean Cooperation in Science and Technology (COST) CA18131 European Commission CP16/00163 project "Information and Communication Technologies for a Single Digital Market in Science, Education and Security" of the Scientific Research Center NIS-3317 National roadmaps for research infrastructures (RIs) NIS-3318 Academy of Finland 295741 H2020-EU.4.b. project "Integration of knowledge and biobank resources in comprehensive translational approach for personalized prevention and treatment of metabolic disorders (INTEGROMED)" 857572 Luxembourg National Research Fund (FNR) CORE grant C18/BM/12585940en_US
dc.language.isoengen_US
dc.publisherFRONTIERS MEDIA SAAVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE CH-1015, SWITZERLANDen_US
dc.relation.isversionof10.3389/fmicb.2021.635781en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmachine learningen_US
dc.subjectmicrobiomeen_US
dc.subjectML4Microbiomeen_US
dc.subjectpersonalized medicineen_US
dc.subjectbiomarker identificationen_US
dc.titleStatistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutionsen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthorBakir-Gungor, Burcu
dc.identifier.volumeVolume 12en_US
dc.relation.journalFRONTIERS IN MICROBIOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US


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