Statistical and Machine Learning Techniques in Human Microbiome Studies

Detalhes bibliográficos
Autor(a) principal: ML4Microbiome
Data de Publicação: 2021
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/115893
Resumo: CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940
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spelling Statistical and Machine Learning Techniques in Human Microbiome StudiesContemporary Challenges and Solutionsbiomarker identificationmachine learningmicrobiomeML4Microbiomepersonalized medicineMicrobiologyMicrobiology (medical)CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The 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.NOVALincsNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNML4Microbiome2021-04-20T22:51:28Z2021-02-222021-02-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/115893eng1664-302XPURE: 29298455https://doi.org/10.3389/fmicb.2021.635781info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:58:28Zoai:run.unl.pt:10362/115893Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:52.599535Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Statistical and Machine Learning Techniques in Human Microbiome Studies
Contemporary Challenges and Solutions
title Statistical and Machine Learning Techniques in Human Microbiome Studies
spellingShingle Statistical and Machine Learning Techniques in Human Microbiome Studies
ML4Microbiome
biomarker identification
machine learning
microbiome
ML4Microbiome
personalized medicine
Microbiology
Microbiology (medical)
title_short Statistical and Machine Learning Techniques in Human Microbiome Studies
title_full Statistical and Machine Learning Techniques in Human Microbiome Studies
title_fullStr Statistical and Machine Learning Techniques in Human Microbiome Studies
title_full_unstemmed Statistical and Machine Learning Techniques in Human Microbiome Studies
title_sort Statistical and Machine Learning Techniques in Human Microbiome Studies
author ML4Microbiome
author_facet ML4Microbiome
author_role author
dc.contributor.none.fl_str_mv NOVALincs
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
RUN
dc.contributor.author.fl_str_mv ML4Microbiome
dc.subject.por.fl_str_mv biomarker identification
machine learning
microbiome
ML4Microbiome
personalized medicine
Microbiology
Microbiology (medical)
topic biomarker identification
machine learning
microbiome
ML4Microbiome
personalized medicine
Microbiology
Microbiology (medical)
description CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940
publishDate 2021
dc.date.none.fl_str_mv 2021-04-20T22:51:28Z
2021-02-22
2021-02-22T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/115893
url http://hdl.handle.net/10362/115893
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1664-302X
PURE: 29298455
https://doi.org/10.3389/fmicb.2021.635781
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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