A toolbox of machine learning software to support microbiome analysis

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dc.authoridDuman, Hatice / 0000-0002-4526-6609
dc.authoridKarav, Sercan / 0000-0003-4056-1673
dc.contributor.authorMarcos-Zambrano, Laura Judith
dc.contributor.authorLopez-Molina, Victor Manuel
dc.contributor.authorBakır-Güngör, Burcu
dc.contributor.authorFrohme, Marcus
dc.contributor.authorKaraduzovic-Hadziabdic, Kanita
dc.contributor.authorKlammsteiner, Thomas
dc.contributor.authorIbrahimi, Eliana
dc.contributor.authorDuman, Hatice
dc.contributor.authorKarav, Sercan
dc.date.accessioned2025-01-27T20:10:31Z
dc.date.available2025-01-27T20:10:31Z
dc.date.issued2023
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
dc.description.sponsorshipCOST Action [CA18131]; COST (European Cooperation in Science and Technology)
dc.description.sponsorshipThis article is based upon work from COST Action ML4Microbiome Statistical and machine learning techniques in human microbiome studies, CA18131, supported by COST (European Cooperation in Science and Technology), www.cost.eu.
dc.identifier.doi10.3389/fmicb.2023.1250806
dc.identifier.issn1664-302X
dc.identifier.pmid38075858
dc.identifier.scopus2-s2.0-85178948402
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3389/fmicb.2023.1250806
dc.identifier.urihttps://hdl.handle.net/20.500.12428/20651
dc.identifier.volume14
dc.identifier.wosWOS:001116447600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media
dc.relation.ispartofFrontiers in Microbiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectmicrobiome
dc.subjectmachine learning
dc.subjectsoftware
dc.subjectfeature generation
dc.subjectfeature analysis
dc.subjectdata integration
dc.subjectmicrobial gene prediction
dc.subjectmicrobial metabolic modeling
dc.titleA toolbox of machine learning software to support microbiome analysis
dc.typeReview Article

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