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Artificial intelligence in microbiome research and beyond: connecting human health, animal husbandry, and aquaculture

Articolo
Data di Pubblicazione:
2025
Abstract:
Technological advancements in computational power and algorithm design have enabled artificial intelligence to become a transformative force in microbiome research. This paper presents a concise overview of recent applications of this computational paradigm in hu‑ man and animal health, with a particular emphasis on aquaculture. International projects focused on the intestinal microbiome have allowed human research to consistently dom‑ inate in terms of application cases, offering insights into various pathological conditions. In contrast, animal research has leveraged artificial intelligence in microbiome analysis to promote sustainable productivity, addressing environmental and public health concerns linked to livestock husbandry. In aquaculture, on the other hand, artificial intelligence has mainly supported management practices, improving rearing conditions and feeding strate‑ gies. When considering microbiome manipulation, however, fish farms have often relied on traditional methods, without harnessing the immense potential of artificial intelligence, whose recent applications include biomonitoring and modeling interactions between mi‑ crobial communities and environmental factors in farming systems. Given the paradigm shift currently underway in both human health and animal husbandry, we advocate for a transition in the aquaculture industry toward smart farming, whose interconnected in‑ frastructure will allow to fully leverage artificial intelligence to seamlessly integrate both biological measurements and rearing parameters.
Tipologia CRIS:
Articolo su Rivista
Keywords:
artificial intelligence; gastrointestinal microbiome; human health; animal health; aquaculture
Elenco autori:
Rizzi, S; Saroglia, G; Kalemi, V; Rimoldi, S; Terova, G.
Autori di Ateneo:
KALEMI VIOLETA
RIMOLDI SIMONA
RIZZI SILVIO
TEROVA GENCIANA
Link alla scheda completa:
https://irinsubria.uninsubria.it/handle/11383/2197074
Link al Full Text:
https://irinsubria.uninsubria.it//retrieve/handle/11383/2197074/443741/Applied%20Science%202025,%20Rizzi,%20Machine%20learning%20and%20microbiota.pdf
Pubblicato in:
APPLIED SCIENCES
Journal
Progetto:
I-FISH - Sviluppo di un sistema intelligente di produzione, distribuzione e tracciabilità di alimenti funzionali a base di pesce
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URL

https://www.mdpi.com/2076-3417/15/17/9781
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