ArticleFrontiers in microbiology2023
Combined network analysis and interpretable machine learning reveals the environmental adaptations of more than 10,000 ruminant microbial genomes.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Cultivation and sequencing of microbiota members unveil the functional potential of yak gut microbiota.mSystems · 2025Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The ruminant gastrointestinal contains numerous microbiomes that serve a crucial role in sustaining the host's productivity and health. In recent times, numerous studies have revealed that variations in influencing factors, including the environment, diet, and host, contribute to the shaping of gastrointestinal microbial adaptation to specific states. Therefore, understanding how host and environmental factors affect gastrointestinal microbes will help to improve the sustainability of ruminant production systems. Results: Based on a graphical analysis perspective, this study elucidates the microbial topology and robustness of the gastrointestinal of different ruminant species, showing that the microbial network is more resistant to random attacks. The risk of transmission of high-risk metagenome-assembled genome (MAG) was also demonstrated based on a large-scale survey of the distribution of antibiotic resistance genes (ARG) in the microbiota of most types of ecosystems. In addition, an interpretable machine learning framework was developed to study the complex, high-dimensional data of the gastrointestinal microbial genome. The evolution of gastrointestinal microbial adaptations to the environment in ruminants were analyzed and the adaptability changes of microorganisms to different altitudes were identified, including microbial transcriptional repair. Conclusion: Our findings indicate that the environment has an impact on the functional features of microbiomes in ruminant. The findings provide a new insight for the future development of microbial resources for the sustainable development in agriculture.
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Registered trials
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