Centro de Investigação em Ciência e Tecnologia das Artes (CITAR)
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- Unraveling the microbiome–environmental change nexus to contribute to a more sustainable world: a comprehensive review of artificial intelligence approachesPublication . Barbosa, Maria Inês; Silva, Gabriel; Ribeiro, Pedro; Vieira, Eduarda; Perrotta, André; Moreira, Patrícia; Rodrigues, Pedro MiguelThis 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.