Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art
Open accessAbstract
The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiota’s role in human health and disease, particularly focusing on CIDs and probiotic interventions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Genomic risk screening can be accelerated using AI algorithms such as DeepMicro, CARD, ResFinder, and machine-learning models identifying specific tRNAs that distinguish safe probiotics from dangerous intestinal pathogens.
“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 [166]
Section 2.1 says AI enables rapid, culture-free microbial identification and describes DeepMicro identifying bacterial species and pathogens from metagenomic data. Section 4 says AI-driven genomic analysis can identify genes associated with antibiotic resistance. Our copy does not support the specific examples CARD or ResFinder, or the claim that models identify tRNAs distinguishing safe probiotics from intestinal pathogens (searched those terms); no page numbers were provided. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Tamtaji, O. R., Taghizadeh, M., Daneshvar Kakhaki, R., Kouchaki, E., Bahmani, F., Borzabadi, S., Oryan, S., Mafi, A., & Asemi, Z. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2018.05.018 · Cited by 398 · see citing works
RetractedTurnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R., & Gordon, J. I. (2007) · Nature · doi:10.1038/nature06244 · Cited by 6,091 · see citing works
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
Revised Estimates for the Number of Human and Bacteria Cells in the Body
Sender, R., Fuchs, S., & Milo, R. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002533 · Cited by 5,425 · see citing works
Role of the microbiota in immunity and inflammation.
Belkaid, Y., & Hand, T. W. (2014) · Cell · doi:10.1016/j.cell.2014.03.011 · Cited by 5,385 · see citing works
The Human Intestinal Microbiome in Health and Disease
Lynch, S. V., & Pedersen, O. (2016) · New England Journal of Medicine · doi:10.1056/nejmra1600266 · Cited by 3,637 · see citing works
Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
Lloyd-Price, J., Arze, C., Ananthakrishnan, A. N., Schirmer, M., Avila-Pacheco, J., Poon, T. W., Andrews, E., Ajami, N. J., Bonham, K. S., Brislawn, C. J., et al. (2019) · Nature · doi:10.1038/s41586-019-1237-9 · Cited by 3,143 · see citing works
Personalized nutrition by prediction of glycemic responses
Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.001 · Cited by 2,929 · see citing works
Environment dominates over host genetics in shaping human gut microbiota
Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., Costea, P. I., Godneva, A., Kalka, I. N., Bar, N., et al. (2018) · Nature · doi:10.1038/nature25973 · Cited by 2,859 · see citing works
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463 · see citing works
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · see citing works
Species-level functional profiling of metagenomes and metatranscriptomes
Franzosa, E. A., McIver, L. J., Rahnavard, G., Thompson, L. R., Schirmer, M., Weingart, G., Lipson, K. S., Knight, R., Caporaso, J. G., Segata, N., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0176-y · Cited by 1,693 · see citing works
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 · see citing works
Ursell, L. K., Metcalf, J. L., Parfrey, L. W., & Knight, R. (2012) · Nutrition Reviews · doi:10.1111/j.1753-4887.2012.00493.x · Cited by 1,255 · see citing works
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
Arango-Argoty, G., Garner, E., Pruden, A. J., Heath, L. S., Vikesland, P. J., & Zhang, L. (2018) · Microbiome · doi:10.1186/s40168-018-0401-z · Cited by 981 · see citing works
Next-generation probiotics: the spectrum from probiotics to live biotherapeutics
O’Toole, P. W., Marchesi, J. R., & Hill, C. (2017) · Nature Microbiology · doi:10.1038/nmicrobiol.2017.57 · Cited by 971 · see citing works
Revised estimates for the number of human and bacteria cells in the bodyPreprint
Sender, R., Fuchs, S., & Milo, R. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/036103 · Cited by 888 · see citing works
Human Gut Microbiome: Function Matters
Heintz‐Buschart, A., & Wilmes, P. (2017) · Trends in Microbiology · doi:10.1016/j.tim.2017.11.002 · Cited by 747 · see citing works
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659 · see citing works
Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities
Zhang, L., & Zhang, L. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3145854 · Cited by 613 · see citing works
Kumar, S., Sharma, D., Rao, S. K., Lim, W. M., & Mangla, S. K. (2022) · Annals of Operations Research · doi:10.1007/s10479-021-04410-8 · Cited by 497 · see citing works
Tursi, A., Brandimarte, G., Papa, A., Giglio, A., Elisei, W., Giorgetti, G. M., Forti, G., Morini, S., Hassan, C., Pistoia, M. A., et al. (2010) · American Journal of Gastroenterology · doi:10.1038/ajg.2010.218 · Cited by 489 · see citing works
Supervised classification of human microbiota
Knights, D., Costello, E. K., & Knight, R. D. (2010) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2010.00251.x · Cited by 482 · see citing works
Marcos-Zambrano, L. J., Karađuzović-Hadžiabdić, K., Lončar-Turukalo, T., Przymus, P., Trajkovik, V., Aasmets, O., Berland, M., Gruca, A., Telalović, J. H., Hron, K., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.634511 · Cited by 429 · see citing works
Knights, D., Ward, T. L., McKinlay, C. E., Miller, H., Gonzalez, A., McDonald, D., & Knight, R. (2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.09.013 · Cited by 396 · see citing works
Human microbiome: an academic update on human body site specific surveillance and its possible role
Dekaboruah, E., Suryavanshi, M. V., Chettri, D., & Verma, A. K. (2020) · Archives of Microbiology · doi:10.1007/s00203-020-01931-x · Cited by 345 · see citing works
Learned protein embeddings for machine learning
Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty178 · Cited by 316 · see citing works
Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
Mallick, H., Franzosa, E. A., Mclver, L. J., Banerjee, S., Sirota‐Madi, A., Kostic, A. D., Clish, C. B., Vlamakis, H. C., Xavier, R. J., & Huttenhower, C. (2019) · Nature Communications · doi:10.1038/s41467-019-10927-1 · Cited by 279 · see citing works
The microbiome quality control project: baseline study design and future directions
Sinha, R. R., Abnet, C. C., White, O. R., Knight, R. D., & Huttenhower, C. (2015) · Genome biology · doi:10.1186/s13059-015-0841-8 · Cited by 267 · see citing works
Personal clinical history predicts antibiotic resistance of urinary tract infections
Yelin, I., Snitser, O., Novich, G., Katz, R., Tal, O., Parizade, M., Chodick, G., Koren, G., Shalev, V., & Kishony, R. (2019) · Nature Medicine · doi:10.1038/s41591-019-0503-6 · Cited by 221 · see citing works
Zamani, B., Golkar, H. R., Farshbaf, S., Emadi‐Baygi, M., Ebrahimi, M. T., Jafari, P., Akhavan, R., Taghizadeh, M., Memarzadeh, M. R., & Asemi, Z. (2016) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.12888 · Cited by 220 · see citing works
DeepMicro: Deep representation learning for disease prediction based on microbiome data
Oh, M., & Zhang, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-63159-5 · Cited by 190 · see citing works
Abavisani, M., Khoshrou, A., Foroushan, S. K., Ebadpour, N., & Sahebkar, A. (2024) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2024.100211 · Cited by 75 · see citing works
Probiotics: Finding the Right Regulatory Balance
Hoffmann, D. E., Fraser, C. M., Palumbo, F. B., Ravel, J., Rothenberg, K., Rowthorn, V., & Schwartz, J. (2013) · Science · doi:10.1126/science.1244656 · Cited by 57 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Zhang, C.; Wang, J.; Li, J.; Liu, X.; Wang, X. Artificial Intelligence in Microbiome Research: Opportunities and Challenges. Front. Microbiol. 2020, 11, 527458.
- Fioravanti, A.; Fustaino, V.; Orsini, M.; Corsi, C.; Arrigo, P.; Patarnello, T.; Carbonneau, M.A.; Mauri, M.; Biondi, E.G. Phylogenetic convolutional neural networks in metagenomics. BMC Bioinform. 2018, 19, 49. [CrossRef] · DOI
- Zhu, Q.; Pan, M.; Liu, J.; Wang, H.; Wei, Y.; Liu, Y.; Xu, Z.; Yang, S.; Wei, H.; Huang, Y.; et al. MicrobioGraph: A Graph Neural Network-Based Framework for Inferring Microbial Interactions from Metagenomic Data. bioRxiv 2021. bioRxiv:2021.02.11.430831.
- Sabico, S.; Al-Mashharawi, A.; Al-Daghri, N.M.; Yakout, S.; Alnaami, A.M.; Alokail, M.S.; McTernan, P.G. Effects of a multi-strain probiotic supplement for 12 weeks in circulating endotoxin levels and cardiometabolic profiles of medication naïve T2DM patients: A randomized clinical trial. J. Transl. Med. 2019, 17, 286. [CrossRef] · DOI
- Franzosa, E.A.; McIver, L.J.; Rahnavard, G.; Thompson, L.R.; Schirmer, M.; Weingart, G.; Lipson, K.S.; Knight, R.; Caporaso, J.G.; Segata, N.; et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 2018, 15, 962-968.
Cited by
Sorted by citations
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 · see citing works
Current Concepts in Probiotic Safety and Efficacy
Churin, A. A., Sokolyanskaya, L. O., Lukina, A. P., & Karnachuk, O. V. (2026) · Nutrients · doi:10.3390/nu18040696 · Cited by 31 · see citing works
Jo, D.-M., Ko, S.-C., Kim, K. W., Yang, D., Kim, J.-Y., Oh, G.-W., Choi, G., Lee, D.-S., Tabassum, N., Kim, Y.-M., et al. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105309 · Cited by 20 · see citing works
- Authors
- D’Urso, F., & Broccolo, F.
- Affiliations
- University of Salento (IT)
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2024-09-25
- Volume
- 14
- Issue
- 19
- Pages
- 8627
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4402824349
- Cited by
- 112
- References
- 35
- Citations by year
- 2026: 59 · 2025: 53
- FWCI
- 15.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- AIRUS (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Keywords
- artificial intelligence, gut microbiota, microbiome analysis, probiotic intervention, metabolomics, transcriptomics, proteomics, genomics, targeted therapy, chronic inflammatory diseases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
