The DOI matches its Crossref record; 204 of the 208 references it cites were found in a registry or an index.
Synergizing artificial intelligence and probiotics: A comprehensive review of emerging applications in health promotion and industrial innovation
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Whole-genome sequencing-based genomic screening allows for the in silico detection of antibiotic resistance genes, virulence-associated factors, and mobile genetic elements, thus providing a basis for modern probiotic safety evaluation [20,133,155,156].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [156]
In the citing paper
“Thus, genomic risk screening can be accelerated through rapid identification using artificial intelligence (AI) algorithms (in particular, DeepMicro, which analyzes sequencing data at high speed to identify bacterial species and potential pathogens with greater speed and accuracy than cultivation-based methods); identification of ARG and virulence detection (for example, using a big data platform”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [156]
In the citing paper
“AI can be used to analyze big data from various omics technologies to create a safety profile for probiotics based on, for example, metabolomics, where AI evaluates metabolite production to ensure that strains do not produce harmful substances associated with various health risks [156,164].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [156]
In the citing paper
“AI plays a major role in the development of predictive toxicology, in which microbial metabolites and their physiological functions can be predicted using a machine learning (ML) model, and potential toxic activity can be identified before in vivo testing [21,156].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [156]
In the citing paper
“The assessment of the degree of safety for vulnerable groups using AI is conducted by analyzing large amounts of data and identifying specific “risk factors” for side effects, such as impaired intestinal barrier function or immunocompromised conditions, which allows for safer use of probiotics [21,156].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [156]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Hill (2014) · doi:10.1038/nrgastro.2014.66 · Cited by 9,592
Introduction to the human gut microbiota
Thursby, E., & Juge, N. (2017) · Biochemical Journal · doi:10.1042/bcj20160510 · Cited by 3,600
Gut microbiota, metabolites and host immunity
Rooks, M. G., & Garrett, W. S. (2016) · Nature reviews. Immunology · doi:10.1038/nri.2016.42 · Cited by 3,409
Enright, M. C., Day, N., Davies, C. E., Peacock, S. J., & Spratt, B. G. (2000) · Journal of Clinical Microbiology · doi:10.1128/jcm.38.3.1008-1015.2000 · Cited by 3,039
A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127
Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023) · Nature Methods · doi:10.1038/s41592-023-01940-w · Cited by 1,705
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
The pros, cons, and many unknowns of probiotics
Suez, J., Zmora, N., Segal, E., & Elinav, E. (2019) · Nature Medicine · doi:10.1038/s41591-019-0439-x · Cited by 1,398
Research works printed but not linked: 1
- Palani, 2024, IntegrIBS: Evaluating the potential and challenges of building a robust IBS classifier with integrated microbiome data, bioRxiv
Cited by
Sorted by citations
Agrawal, K., Göktaş, P., Kumar, N., & Leung, M.-F. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1636980 · Cited by 140
Gangakhedkar, P. S., Deshpande, H. W., Törős, G., El-Ramady, H. R., Elsakhawy, T. A., Abdalla, N. A., Shaikh, A. M., Kovács, B., Mane, R. P., & Prokisch, J. (2025) · Foods · doi:10.3390/foods14132155 · Cited by 40
Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era
Asar, R., Erenler, S., Devecioglu, D., Ispirli, H., Karbancioglu-Guler, F., Ozturk, H. I., & Dertli, E. (2025) · Fermentation · doi:10.3390/fermentation11050259 · Cited by 37
Precision Microbiome: A New Era of Targeted Therapy with Core Probiotics
Fang, X., Wang, Y., Wei, H., & Huang, Y. (2025) · Research · doi:10.34133/research.0658 · Cited by 34
Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine
Alexandrescu, L., Tofolean, I. T., Condur, L., Tofolean, D. E., Nicoara, A. D., Șerbănescu, L., Rusu, E. E., Twakor, A. N., Dumitru, E., Dumitru, A., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12090944 · Cited by 28
Metadata
- Authors
- Han, X., Liu, Q., Li, Y., Zhang, M., Liu, K., Kwok, L.-Y., Zhang, H., & Zhang, W.
- Affiliations
- Inner Mongolia Agricultural University (CN)
- Corresponding author
- Zhang, Meng
- Venue
- Trends in Food Science & Technology
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 159
- Pages
- 104938
- ISSN
- 0924-2244 (ISSN-L)
- OpenAlex
- W4407923135
- Cited by
- 64
- References
- 208
- Citations by year
- 2026: 43 · 2025: 21
- FWCI
- 30.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 337 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Trends in Food Science & Technology (acceptedVersion)
- Funders
- National Natural Science Foundation of China (32372304)
- Government of Inner Mongolia Autonomous Region
- Topics
- Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, probiotics, machine learning, biomarker prediction, probiotic metabolites, antimicrobial peptides, exopolysaccharides, phenolic compounds, personalized nutrition, inflammatory bowel disease, irritable bowel syndrome, gut microbiota, functional foods, personalized medicine, fermentation technology, bacterial identification
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