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Unraveling the microbiome–environmental change nexus to contribute to a more sustainable world: a comprehensive review of artificial intelligence approaches

2025-08-09Artigo de revisão Acesso aberto
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datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg06:Água Potável e Saneamento
datacite.subject.sdg15:Proteger a Vida Terrestre
dc.contributor.authorBarbosa, Maria Inês
dc.contributor.authorSilva, Gabriel
dc.contributor.authorRibeiro, Pedro
dc.contributor.authorVieira, Eduarda
dc.contributor.authorPerrotta, André
dc.contributor.authorMoreira, Patrícia
dc.contributor.authorRodrigues, Pedro Miguel
dc.date.accessioned2025-09-08T09:43:15Z
dc.date.available2025-09-08T09:43:15Z
dc.date.issued2025-08-09
dc.description.abstractThis review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability.eng
dc.identifier.citationBarbosa, M. I., Silva, G., Ribeiro, P., & Vieira, E. et al. (2025). Unraveling the microbiome–environmental change nexus to contribute to a more sustainable world: a comprehensive review of artificial intelligence approaches. Sustainability, 17(16), Article 7209. https://doi.org/10.3390/su17167209
dc.identifier.doi10.3390/su17167209
dc.identifier.eid105014255237
dc.identifier.issn2071-1050
dc.identifier.other506b601a-f5a8-4469-8ba2-e7d2e20d6eae
dc.identifier.urihttp://hdl.handle.net/10400.14/54725
dc.identifier.wos001558403100001
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learning
dc.subjectEnvironment
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectMicrobiome
dc.subjectSustainability goals
dc.titleUnraveling the microbiome–environmental change nexus to contribute to a more sustainable world: a comprehensive review of artificial intelligence approacheseng
dc.typereview article
dspace.entity.typePublication
oaire.citation.issue16
oaire.citation.titleSustainability
oaire.citation.volume17
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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