Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

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creativework.keywordsbiomarker identification, disease prediction, feature selection, machine learning, microbiome
creativework.publisherFrontiers Media S.A.en
dc.contributor.authorMarcos-Zambrano L.J.
dc.contributor.authorKaraduzovic-Hadziabdic K.
dc.contributor.authorLoncar Turukalo T.
dc.contributor.authorPrzymus P.
dc.contributor.authorTrajkovik V.
dc.contributor.authorAasmets O.
dc.contributor.authorBerland M.
dc.contributor.authorGruca A.
dc.contributor.authorHasic J.
dc.contributor.authorHron K.
dc.contributor.authorKlammsteiner T.
dc.contributor.authorKolev M.
dc.contributor.authorLahti L.
dc.contributor.authorLopes M.B.
dc.contributor.authorMoreno V.
dc.contributor.authorNaskinova I.
dc.contributor.authorOrg E.
dc.contributor.authorPaciência I.
dc.contributor.authorPapoutsoglou G.
dc.contributor.authorShigdel R.
dc.contributor.authorStres B.
dc.contributor.authorVilne B.
dc.contributor.authorYousef M.
dc.contributor.authorZdravevski E.
dc.contributor.authorTsamardinos I.
dc.contributor.authorCarrillo de Santa Pau E.
dc.contributor.authorClaesson M.J.
dc.contributor.authorMoreno-Indias I.
dc.contributor.authorTruu J.
dc.date.accessioned2024-12-04T13:38:25Z
dc.date.accessioned2024-12-04T13:46:57Z
dc.date.available2024-12-04T13:38:25Z
dc.date.available2024-12-04T13:46:57Z
dc.date.issued2021-02-19
dc.description.abstractThe number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
dc.identifier.doi10.3389/fmicb.2021.634511
dc.identifier.issn1664-302X
dc.identifier.scopusSCOPUS_ID:85102368878en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/1548
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102368878&origin=inward
dc.titleApplications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment
dc.typeReview
oaire.citation.volume12
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