Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era
Open accessAbstract
This review focuses on the potential utilization of artificial intelligence (AI) tools to deepen our understanding of probiotics, their mode of action, and technological characteristics such as survival. To that end, this review provides an overview of the current knowledge on probiotics as well as next-generation probiotics. AI-aided omics technologies, including genomics, transcriptomics, and proteomics, offer new insights into the genetic and functional properties of probiotics. Furthermore, AI can be used to elucidate key probiotic activities such as microbiota modulation, metabolite production, and immune system interactions to enable an improved understanding of their health impacts. Additionally, AI technologies facilitate precision in identifying probiotic health impacts, including their role in gut health, anticancer activity, and antiaging effects. Beyond health applications, AI can expand the technological use of probiotics, optimizing storage survival and broadening biotechnological approaches. In this context, this review addresses how AI-driven approaches can be facilitated by strengthening the evaluation of probiotic characteristics, explaining their mechanisms of action, and enhancing their technological applications. Moreover, the potential of AI to enhance the precision of probiotic health impact assessments and optimize industrial applications is highlighted, concluding with future perspectives on the transformative role of AI in probiotic research.
Integrity signals
Registry
Cites
Statistics
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?
AI capabilities are fueling probiogenomics, where computational models predict how a probiotic's genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects.
“The growth of AI capabilities is fueling new fields, such as probiogenomics, where computational models are used to predict how a probiotic’s genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects [164,169].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [164]
AI can analyze big data from omics technologies to create a safety profile for probiotics based on metabolomics, evaluating metabolite production to ensure strains do not produce harmful substances associated with health risks.
“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 [164]
Computational tools are evolving and becoming integrated into regulatory processes, where genomic and bioinformatic analysis complement phenotypic testing, highlighting the need for standardized, transparent, and validated computational processes.
“These computational tools are evolving and becoming integrated into regulatory processes over time, where genomic and bioinformatic analysis complement phenotypic testing to facilitate safety assessment and documentation, and highlight the need for standardized, transparent, and validated computational processes [163,164].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [164]
Cites
Hill (2014) · doi:10.1038/nrgastro.2014.66 · Cited by 9,592 · see citing works
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · 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
Probiotics Mechanism of Action on Immune Cells and Beneficial Effects on Human Health
Mazziotta, C., Tognon, M., Martini, F., Torreggiani, E., & Rotondo, J. C. (2023) · Cells · doi:10.3390/cells12010184 · Cited by 889 · see citing works
Carbohydrate metabolism in Bifidobacteria
Pokusaeva, K., Fitzgerald, G. F., & van Sinderen, D. (2011) · Genes & Nutrition · doi:10.1007/s12263-010-0206-6 · Cited by 855 · see citing works
Kankainen, M., Paulin, L., Tynkkynen, S., von Ossowski, I., Reunanen, J., Partanen, P., Satokari, R., Vesterlund, S., Hendrickx, A. P. A., Lebeer, S., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908876106 · Cited by 740 · 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
Shaping the Future of Probiotics and Prebiotics
Cunningham, M., Azcarate-Peril, M. A., Barnard, A., Benoit, V., Grimaldi, R., Guyonnet, D., Holscher, H. D., Hunter, K., Manurung, S., Obis, D., et al. (2021) · Trends in Microbiology · doi:10.1016/j.tim.2021.01.003 · Cited by 637 · see citing works
Durazzi, F., Sala, C., Castellani, G., Manfreda, G., Remondini, D., & Cesare, A. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-82726-y · Cited by 599 · see citing works
Advances and Trends in Omics Technology Development
Dai, X., & Shen, L. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.911861 · Cited by 567 · see citing works
Human gut microbiome: the second genome of human body
Zhu, B., Wang, X., & Li, L. (2010) · Protein & Cell · doi:10.1007/s13238-010-0093-z · Cited by 535 · see citing works
Immune–neural connections: how the immune system’s response to infectious agents influences behavior
McCusker, R. H., & Kelley, K. W. (2013) · Journal of Experimental Biology · doi:10.1242/jeb.073411 · Cited by 429 · 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
Artificial intelligence (AI) and big data in cancer and precision oncology
Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.019 · Cited by 412 · see citing works
The Potential Impact of Probiotics on Human Health: An Update on Their Health-Promoting Properties
Maftei, N.-M., Raileanu, C. R., Balta, A. A., Ambrose, L., Boev, M., Marin, D. B., & Lisa, E. L. (2024) · Microorganisms · doi:10.3390/microorganisms12020234 · Cited by 381 · see citing works
Probiotic: conceptualization from a new approach
Zendeboodi, F., Khorshidian, N., Mortazavian, A. M., & da Cruz, A. G. (2020) · Current Opinion in Food Science · doi:10.1016/j.cofs.2020.03.009 · Cited by 378 · see citing works
Iqbal, M. J., Javed, Z., Sadia, H., Qureshi, I. A., Irshad, A., Ahmed, R., Malik, K. A., Raza, S., Abbas, A., Pezzani, R., et al. (2021) · Cancer Cell International · doi:10.1186/s12935-021-01981-1 · Cited by 369 · see citing works
Machine learning and deep learning applications in microbiome research
Medina, R. H., Kutuzova, S., Nielsen, K. N., Johansen, J., Hansen, L. H., Nielsen, M. E., & Rasmussen, S. (2022) · ISME Communications · doi:10.1038/s43705-022-00182-9 · Cited by 356 · see citing works
Sugimura, N., Li, Q., Chu, E. S., Lau, H. C.-H., Fong, W., Liu, W., Liang, C., Nakatsu, G., Su, A. C. Y., Coker, O. O., et al. (2021) · Gut · doi:10.1136/gutjnl-2020-323951 · Cited by 288 · see citing works
Role of Gut Microbiota, Probiotics and Prebiotics in the Cardiovascular Diseases
Oniszczuk, A., Oniszczuk, T., Gancarz, M., & Szymańska, J. (2021) · Molecules · doi:10.3390/molecules26041172 · Cited by 280 · see citing works
Evaluating Machine Learning Models and Their Diagnostic Value
Varoquaux, G., & Colliot, O. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_20 · Cited by 242 · see citing works
A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers
Takahashi, M. K., Tan, X., Dy, A. J., Braff, D., Akana, R., Furuta, Y., Donghia, N. M., Ananthakrishnan, A. N., & Collins, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-05864-4 · Cited by 238 · see citing works
Holčapek, M., Liebisch, G., & Ekroos, K. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b05395 · Cited by 233 · see citing works
Machine learning and applications in microbiology
Goodswen, S. J., Barratt, J., Kennedy, P. J., Kaufer, A., Calarco, L., & Ellis, J. (2021) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuab015 · Cited by 213 · see citing works
Next-generation probiotics: the upcoming biotherapeutics
Al-Fakhrany, O. M., & Elekhnawy, E. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-09398-5 · Cited by 201 · see citing works
In vitro test to evaluate survival in the gastrointestinal tract of commercial probiotics
Naissinger da Silva, M., Tagliapietra, B. L., Flores, V. D. A., & Pereira dos Santos Richards, N. S. (2021) · Current Research in Food Science · doi:10.1016/j.crfs.2021.04.006 · Cited by 198 · 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
Effects of probiotics on type II diabetes mellitus: A meta-analysis
Tao, Y.-W., Gu, Y.-L., Mao, X.-Q., Zhang, L., & Pei, Y.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02213-2 · Cited by 173 · see citing works
Survival of Microencapsulated Probiotic Bacteria after Processing and during Storage: A Review
Dianawati, D., Mishra, V. K., & Shah, N. P. (2015) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2013.798779 · Cited by 170 · see citing works
Sasikumar, K., Kozhummal Vaikkath, D., Devendra, L., & Nampoothiri, K. M. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.05.075 · Cited by 169 · see citing works
Bacterial Signaling to the Nervous System through Toxins and Metabolites
Yang, N. J., & Chiu, I. M. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.12.023 · Cited by 165 · see citing works
Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review
Vahabi, N., & Michailidis, G. C. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.854752 · Cited by 159 · see citing works
Machine learning for data integration in human gut microbiome
Li, P., Luo, H., Ji, B., & Nielsen, J. (2022) · Microbial Cell Factories · doi:10.1186/s12934-022-01973-4 · Cited by 150 · see citing works
Wan, Y., Zuo, T., Xu, Z., Zhang, F., Zhan, H., Dorothy, C. H. A. N., Leung, T.-F., Yeoh, Y. K., Chan, F. K. L., Chan, R., et al. (2021) · Gut · doi:10.1136/gutjnl-2020-324015 · Cited by 148 · see citing works
Plaza‐Díaz, J., Solis‐Urra, P., Rodríguez‐Rodríguez, F., Arancibia, J. E. O., Navarro-Oliveros, M., Abadía‐Molina, F., & Álvarez‐Mercado, A. I. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218351 · Cited by 147 · see citing works
Chang, H. M., Foo, H. L., Loh, T. C., Lim, E. T. C., & Mutalib, N. E. A. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2020.602280 · Cited by 140 · see citing works
‘Multi-omics’ data integration: applications in probiotics studies
Kwoji, I. D., Aiyegoro, O. A., Okpeku, M., & Adeleke, M. A. (2023) · npj Science of Food · doi:10.1038/s41538-023-00199-x · Cited by 135 · see citing works
Deep reinforcement learning for the control of microbial co-cultures in bioreactors
Treloar, N. J., Fedorec, A. J. H., Ingalls, B., & Barnes, C. P. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007783 · Cited by 131 · see citing works
Bilal, H., Khan, M. N., Khan, S., Shafiq, M., Fang, W., Khan, R. U., Rahman, M. U., Li, X., Lv, Q., & Xu, B. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.006 · Cited by 122 · see citing works
Development of Nanosensors Based Intelligent Packaging Systems: Food Quality and Medicine
Chelliah, R., Wei, S., Daliri, E. B., Rubab, M., Elahi, F., Yeon, S., Jo, K. H., Yan, P., Liu, S., & Oh, D. H. (2021) · Nanomaterials · doi:10.3390/nano11061515 · Cited by 120 · see citing works
Safety aspects of next generation probiotics
Saarela, M. H. (2019) · Current Opinion in Food Science · doi:10.1016/j.cofs.2018.09.001 · Cited by 118 · see citing works
Thananimit, S., Pahumunto, N., & Teanpaisan, R. (2022) · Biomolecules · doi:10.3390/biom12121829 · Cited by 117 · see citing works
Nehal, F., Sahnoun, M., Smaoui, S., Jaouadi, B., Béjar, S., & Sebaihia, M. (2019) · Microbial Pathogenesis · doi:10.1016/j.micpath.2019.04.018 · Cited by 116 · see citing works
Research progress on improving the freeze-drying resistance of probiotics: A review
Ge, S., Han, J., Sun, Q., Zhou, Q., Ye, Z., Li, P., & Gu, Q. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104425 · Cited by 114 · see citing works
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
Sun, Y., Li, H., Zheng, L., Li, J., Hong, Y., Liang, P., Kwok, L., Zuo, Y., Zhang, W., & Zhang, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab477 · Cited by 112 · see citing works
D’Urso, F., & Broccolo, F. (2024) · Applied Sciences · doi:10.3390/app14198627 · Cited by 112 · see citing works
Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota
Rozera, T., Pasolli, E., Segata, N., & Ianiro, G. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.02.035 · Cited by 112 · see citing works
Barzegar, H., Alizadeh Behbahani, B., & Falah, F. (2021) · Food Science & Nutrition · doi:10.1002/fsn3.2365 · Cited by 108 · see citing works
Yépez, A., Russo, P., Spano, G., Khomenko, I., Franco Biasioli, A., Capozzi, V., & Aznar, R. (2018) · Food Microbiology · doi:10.1016/j.fm.2018.08.008 · Cited by 106 · see citing works
Liao, W., Shen, J., Manickam, S., et al. (2022) · Food Chemistry · doi:10.1016/j.foodchem.2022.134982 · Cited by 102 · see citing works
Zhang, X., Hindra, F., & Elliot, M. A. (2019) · Current Opinion in Microbiology · doi:10.1016/j.mib.2019.03.003 · Cited by 98 · see citing works
Practical considerations for active machine learning in drug discovery
Reker, D. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.06.001 · Cited by 95 · see citing works
Harnessing machine learning for development of microbiome therapeutics
McCoubrey, L. E., Elbadawi, M., Orlu, M., Gaisford, S., & Basit, A. W. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1872323 · Cited by 94 · see citing works
Sadeghi, M., Panahi, B., Mazlumi, A., Hejazi, M. A., Komi, D. E. A., & Nami, Y. (2022) · LWT · doi:10.1016/j.lwt.2022.113471 · Cited by 92 · see citing works
Wu, S., Feng, J., Liu, C., Wu, H., Qiu, Z., Ge, J., Sun, S., Hong, X., Li, Y., Wang, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30741-6 · Cited by 91 · see citing works
Wang, Y., Liang, Q., Lu, B., Shen, H., Liu, S., Shi, Y., Leptihn, S., Li, H., Wei, J., Liu, C., et al. (2021) · BMC Genomics · doi:10.1186/s12864-021-07539-9 · Cited by 88 · see citing works
KHALIL, M. A. E. F., Sonbol, F., Al-Madboly, L. A., Aboshady, T. A., Alqurashi, A. S., & Ali, S. S. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.803688 · Cited by 86 · see citing works
Golnari, M., Bahrami, N., Milanian, Z., Rabbani Khorasgani, M., Asadollahi, M. A., Shafiei, R., & Fatemi, S. S.-A. (2024) · Scientific Reports · doi:10.1038/s41598-024-51823-z · Cited by 86 · see citing works
Kolmeder, C. A., Salojärvi, J., Ritari, J., de Been, M., Raes, J., Falony, G., Vieira‐Silva, S., Kekkonen, R. A., Corthals, G. L., Palva, A., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153294 · Cited by 83 · see citing works
Machine Learning: Data Pre‐processing
Kang, M., & Tian, J. (2018) · doi:10.1002/9781119515326.ch5 · Cited by 81 · see citing works
Artificial intelligence-based personalized diet: A pilot clinical study for irritable bowel syndrome
Karakan, T., Gündoğdu, A., Alagözlü, H., Ekmen, N., Özgül, S., Tunalı, V., Hora, M., Beyazgül, D., & Nalbantoğlu, Ö. U. (2022) · Gut Microbes · doi:10.1080/19490976.2022.2138672 · Cited by 80 · see citing works
Yang, S., Bai, M., Kwok, L., Zhong, Z., & Sun, Z. (2023) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2023.2280706 · Cited by 80 · see citing works
Carasi, P., Racedo, S., Jacquot, C., Elie, A. M., Serradell, M. D. L. Á., & Urdaci, M. C. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00088 · Cited by 75 · 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
Artificial intelligence and synthetic biology approaches for human gut microbiome
Kumar, P., Sinha, R., & Shukla, P. (2020) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2020.1850415 · Cited by 74 · see citing works
Metagenomics and artificial intelligence in the context of human health
Wani, A. K., Roy, P., Gahlawat, V. K., & Mir, T. U. G. (2022) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2022.105267 · Cited by 72 · see citing works
A new horizon of precision medicine: combination of the microbiome and extracellular vesicles
Yang, J., Shin, T.-S., Kim, J. S., Jee, Y., & Kim, Y. (2022) · Experimental & Molecular Medicine · doi:10.1038/s12276-022-00748-6 · Cited by 72 · see citing works
Schoug, Å., Fischer, J., Heipieper, H. J., Schnürer, J., & Håkansson, S. (2007) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-007-0281-x · Cited by 71 · see citing works
Quinoa pasta fermented with lactic acid bacteria prevents nutritional deficiencies in mice
Carrizo, S. L., de LeBlanc, A. D. M., LeBlanc, J. G., & Rollán, G. (2019) · Food Research International · doi:10.1016/j.foodres.2019.108735 · Cited by 71 · see citing works
Westfall, S., Carracci, F., Estill, M. S., Zhao, D., Wu, Q.-L., Shen, L., Simon, J. E., & Pasinetti, G. M. (2021) · Scientific Reports · doi:10.1038/s41598-020-79947-y · Cited by 71 · see citing works
LipidSig: a web-based tool for lipidomic data analysis
Lin, W.-J., Shen, P.-C., Liu, H.-C., Cho, Y.-C., Hsu, M.-K., Lin, I.-C., Chen, G. T. Y., Yang, J., Ma, W., & Cheng, W. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab419 · Cited by 70 · see citing works
Rugji, J., Erol, Z., Taşçı, F., Musa, L., Hamadani, A., Gündemir, M. G., Karalliu, E., & Siddiqui, S. A. (2024) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2024.2430749 · Cited by 68 · see citing works
Probiotics: Health benefits, food application, and colonization in the human gastrointestinal tract
Lau, L. Y. J., & Quek, S. Y. (2024) · Food Bioengineering · doi:10.1002/fbe2.12078 · Cited by 64 · see citing works
Han, X., Liu, Q., Li, Y., Zhang, M., Liu, K., Kwok, L.-Y., Zhang, H., & Zhang, W. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104938 · Cited by 64 · see citing works
Proteomic analysis of cell surface-associated proteins from probioticLactobacillus plantarum
Beck, H. C., Madsen, S. ̧. M., Glenting, J., Petersen, J., Israelsen, H., ̧rrelykke, M. R. N., Antonsson, M., & Hansen, A. M. (2009) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2009.01662.x · Cited by 63 · see citing works
Leandro, E. D. S., Ginani, V. C., de Alencar, E. R., Pereira, O. G., Rose, E. C. P., do Vale, H. M. M., Pratesi, R., Hecht, M. M., Cavalcanti, M. H., & Tavares, C. S. O. (2020) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-020-09679-y · Cited by 63 · see citing works
Kumar, R., Bansal, P., Singh, J., & Dhanda, S. (2020) · Process Biochemistry · doi:10.1016/j.procbio.2020.08.028 · Cited by 63 · see citing works
Artificial Intelligence in Omics
Gao, F., Huang, K., & Xing, Y. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.01.002 · Cited by 63 · see citing works
Biotechnological approaches for the production of designer cheese with improved functionality
Chourasia, R., Abedin, M. M., Phukon, L. C., Sahoo, D., Singh, S. P., & Kumar, A. (2020) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.12680 · Cited by 62 · see citing works
Complete genome analysis of Lactobacillus fermentum YLF016 and its probiotic characteristics
Zhang, L., Ma, H., Kulyar, M. F., Pan, H., Li, K., Li, J., Mo, Q., Wang, Y., Dong, H., Bao, Y., et al. (2021) · Microbial Pathogenesis · doi:10.1016/j.micpath.2021.105212 · Cited by 61 · see citing works
Multi-omics study for interpretation of genome-wide association study
Akiyama, M. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-00842-5 · Cited by 60 · see citing works
Zhao, Y., Wu, Z., MIYAO, S., et al. (2022) · Food Bioscience · doi:10.1016/j.fbio.2022.101815 · Cited by 59 · see citing works
Park, J. M., Lee, S. C., Ham, C., & Kim, Y. W. (2023) · Gut Pathogens · doi:10.1186/s13099-023-00536-1 · Cited by 59 · see citing works
Active Machine learning for formulation of precision probiotics
McCoubrey, L. E., Seegobin, N., Elbadawi, M., Hu, Y., Orlu, M., Gaisford, S., & Basit, A. W. (2022) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2022.121568 · Cited by 56 · see citing works
Beneficial Bacteria Isolated from Food in Relation to the Next Generation of Probiotics
Sionek, B., Szydłowska, A., Zielińska, D., Neffe‐Skocińska, K., & Kołożyn‐Krajewska, D. (2023) · Microorganisms · doi:10.3390/microorganisms11071714 · Cited by 54 · see citing works
Proteomics for the Investigation of Surface-Exposed Proteins in Probiotics
Siciliano, R. A., Lippolis, R., & Mazzeo, M. F. (2019) · Frontiers in Nutrition · doi:10.3389/fnut.2019.00052 · Cited by 51 · see citing works
García-González, N., Bottacini, F., van Sinderen, D., Gahan, C. G. M., & Corsetti, A. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.854266 · Cited by 49 · see citing works
Chen, H., Tian, M., Chen, L., Cui, X., Meng, J., & Shu, G. (2019) · Artificial Cells Nanomedicine and Biotechnology · doi:10.1080/21691401.2019.1603157 · Cited by 48 · see citing works
Machine learning-based approaches for cancer prediction using microbiome data
Freitas, P., Silva, F., Sousa, J. V., Ferreira, R. M., Figueiredo, C., Pereira, T., & Oliveira, H. P. (2023) · Scientific Reports · doi:10.1038/s41598-023-38670-0 · Cited by 48 · see citing works
Amenu, D., & Bacha, K. (2023) · Annals of Microbiology · doi:10.1186/s13213-023-01740-9 · Cited by 48 · see citing works
Isolation and Characterization of a Cholesterol-Lowering Bacteria from Bubalus bubalis Raw Milk
Hameed, A., Condò, C., Tauseef, I., Idrees, M., Ghazanfar, S., Farid, A., Muzammal, M., Mohaini, M. A., Alsalman, A. J., Hawaj, M. A. A., et al. (2022) · Fermentation · doi:10.3390/fermentation8040163 · Cited by 47 · see citing works
Abdel‐Nasser, A., Hathout, A. S., Badr, A. N., Barakat, O. S., & Fathy, H. M. (2023) · Biotechnology Reports · doi:10.1016/j.btre.2023.e00799 · Cited by 47 · see citing works
Aggregation and adhesion ability of various probiotic strains and Candida species: An in vitro study
Chantanawilas, P., Pahumunto, N., & Teanpaisan, R. (2024) · Journal of Dental Sciences · doi:10.1016/j.jds.2024.03.016 · Cited by 46 · cited 1× · see citing works
“Another crucial characteristic of probiotics is their hydrophobicity, which influences the aggregation and adhesion capabilities of microorganisms [45].”
Das, A., Behera, R. N., Kapoor, A., & Ambatipudi, K. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c03834 · Cited by 44 · see citing works
metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model
Wu, S., Feng, T., Tang, W., Qi, C., Gao, J., He, X., Wang, J., Zhou, H., & Fang, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae085 · Cited by 43 · see citing works
Kim, Y., Choi, S., Jeong, Y., & Kang, C.-H. (2022) · Microorganisms · doi:10.3390/microorganisms10102070 · Cited by 40 · see citing works
Kim, J.-Y., Choi, E.-J., Lee, J. H., Yoo, M.-S., Heo, K., Shim, J.-J., & Lee, J.-L. (2021) · Applied Sciences · doi:10.3390/app11135765 · Cited by 39 · see citing works
A cost-aware framework for the development of AI models for healthcare applications
Erion, G., Janizek, J. D., Hudelson, C. E., Utarnachitt, R. B., McCoy, A. M., Sayre, M. R., White, N. J., & Lee, S. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00872-8 · Cited by 39 · see citing works
Emerging concepts in non-invasive monitoring of Crohn’s disease
Marlicz, W. M., Skonieczna‐Żydecka, K., Dabos, K. J., Łoniewski, I., & Koulaouzidis, A. (2018) · Therapeutic Advances in Gastroenterology · doi:10.1177/1756284818769076 · Cited by 38 · see citing works
Khromova, N. Y., Epishkina, J. M., Karetkin, B. A., Khabibulina, N. V., Beloded, A. V., Shakir, I. V., & Panfilov, V. I. (2022) · Microorganisms · doi:10.3390/microorganisms10020470 · Cited by 38 · see citing works
The Landscape and Perspectives of the Human Gut Metaproteomics
Sun, Z.-Z., Ning, Z., & Figeys, D. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100763 · Cited by 38 · see citing works
Afreen, A., Ahmed, Z., Khalid, N., Ferheen, I., & Ahmed, I. (2023) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-023-12372-z · Cited by 37 · see citing works
Development and reporting of artificial intelligence in osteoporosis management
Gatineau, G., Shevroja, E., Vendrami, C., et al. (2024) · Journal of Bone and Mineral Research · doi:10.1093/jbmr/zjae131 · Cited by 36 · see citing works
An Intelligent Intestine‐on‐a‐Chip for Rapid Screening of Probiotics with Relief‐Enteritis Function
Wu, J., Zhang, B., Liu, X. D., Gu, W., Xu, F., Wang, J., Liu, Q., Wang, R., Hu, Y., Liu, J., et al. (2024) · Advanced Materials · doi:10.1002/adma.202408485 · Cited by 36 · see citing works
Fermented Foods as a Potential Vehicle of Antimicrobial-Resistant Bacteria and Genes
Vinayamohan, P. G., Viju, L. S., Joseph, D., & Venkitanarayanan, K. (2023) · Fermentation · doi:10.3390/fermentation9070688 · Cited by 35 · see citing works
Bertorello, S., Cei, F., Fink, D., Niccolai, E., & Amedei, A. (2024) · Microorganisms · doi:10.3390/microorganisms12091828 · Cited by 35 · see citing works
Krupitzer, C. (2024) · Frontiers in Food Science and Technology · doi:10.3389/frfst.2024.1473357 · Cited by 35 · see citing works
Oudat, Q., & Okour, A. (2025) · Acta Microbiologica Hellenica · doi:10.3390/amh70010005 · Cited by 35 · see citing works
Sentürk, M., Ercan, F. S., & Yalçın, S. (2019) · Cancer Chemotherapy and Pharmacology · doi:10.1007/s00280-019-03978-0 · Cited by 34 · see citing works
Jeong, S., Kim, Y., Park, S., Lee, D., Lee, J., Hlaing, S. P., Yoo, J., Rhee, S. H., & Im, E. (2023) · Molecules · doi:10.3390/molecules28041890 · Cited by 34 · see citing works
AI-driven insights into the microbiota: Figuring out the mysterious world of the gut
Patil, A., Patil, A., Singh, N., et al. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.08.003 · Cited by 34 · see citing works
Applying AI Tools for Modeling, Predicting and Managing the White Wine Fermentation Process
Florea, A., Șipoș, A., & Stoisor, M.-C. (2022) · Fermentation · doi:10.3390/fermentation8040137 · Cited by 33 · see citing works
Hao, X., Yu, X., Wang, Y., et al. (2022) · International Dairy Journal · doi:10.1016/j.idairyj.2022.105507 · Cited by 33 · see citing works
Ali, A. H., Bamigbade, G. B., Tarique, M., Esposito, G., Obaid, R. S., Abu‐Jdayil, B., & Ayyash, M. M. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.124425 · Cited by 31 · see citing works
Bianchi, L., Laghi, L., Correani, V., Schifano, E., Landi, C., Uccelletti, D., & Mattei, B. (2020) · Biomolecules · doi:10.3390/biom10010157 · Cited by 30 · see citing works
It takes guts to learn: machine learning techniques for disease detection from the gut microbiome
Curry, K. D., Nute, M., & Treangen, T. J. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210213 · Cited by 30 · see citing works
Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence
Loddo, A., Ruberto, C. D., Armano, G., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3223710 · Cited by 30 · see citing works
Poimenidou, S. V., Skarveli, A., Saxami, G., Mitsou, E. K., Kotsou, M., & Kyriacou, A. (2023) · Microorganisms · doi:10.3390/microorganisms11020363 · Cited by 30 · see citing works
Lalowski, P., & Zielińska, D. (2024) · Fermentation · doi:10.3390/fermentation10090444 · Cited by 30 · see citing works
Klotz, C., O’Flaherty, S., Goh, Y. J., & Barrangou, R. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02174 · Cited by 29 · see citing works
Gawande, K., Kolhekar, M., Kumari, M., Kapila, S., Sharma, P., Ali, S. A., & Behare, P. V. (2021) · Food Hydrocolloids for Health · doi:10.1016/j.fhfh.2021.100042 · Cited by 29 · see citing works
Kang, S., Kim, H., Kim, S., Kim, J., Park, S., Lee, C.-W., Kim, K. O., Seo, G. S., Kim, M. S., Cha, J. M., et al. (2023) · Microorganisms · doi:10.3390/microorganisms11071665 · Cited by 29 · see citing works
Zhang, X., Zheng, Y., Liu, Z., et al. (2024) · Food Chemistry X · doi:10.1016/j.fochx.2024.101147 · Cited by 29 · see citing works
Next-generation probiotics: Innovations in safety assessments
Hua, Z., Liu, S., Yang, G., Hou, X., & Fang, Y. (2025) · Current Opinion in Food Science · doi:10.1016/j.cofs.2024.101238 · Cited by 29 · see citing works
Ain, N. U., Naveed, M., Aziz, T. A., Shabbir, M. A., Al‐Asmari, F., Abdi, G., Sameeh, M. Y., & Alhhazmi, A. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-69515-z · Cited by 28 · see citing works
Yue, L., Song, L., Zhu, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-023-50832-8 · Cited by 27 · see citing works
Feng, Y., Soni, A., Brightwell, G., dos Reis, M. M., Wang, Z., Wang, J., Wu, Q., & Ding, Y. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104555 · Cited by 27 · see citing works
Gül, L. B., Gül, O., Yılmaz, M. T., et al. (2020) · Journal of Food Processing and Preservation · doi:10.1111/jfpp.14400 · Cited by 26 · see citing works
Gizachew, S., Van Beeck, W., Spacova, I., Dekeukeleire, M., Alemu, A., Woldemedhin, W. M., Mariam, S. H., Lebeer, S., & Engidawork, E. (2023) · Fermentation · doi:10.3390/fermentation9030258 · Cited by 26 · see citing works
Josephs‐Spaulding, J., Rajput, A., Hefner, Y., Szubin, R., Balasubramanian, A., Li, G., Zielinski, D. C., Jahn, L. J., Sommer, M., Phaneuf, P. V., et al. (2024) · mSystems · doi:10.1128/msystems.01257-23 · Cited by 26 · see citing works
Capsule robots for the monitoring, diagnosis, and treatment of intestinal diseases
Wei, X., Xi, P., Chen, M., Wen, Y., Hao, W., Wang, L., Zhu, Y., Ren, Y., & Gu, Z. (2024) · Materials Today Bio · doi:10.1016/j.mtbio.2024.101294 · Cited by 26 · see citing works
Anitha, P., Anbarasu, A., & Ramaiah, S. (2015) · Journal of Cellular Biochemistry · doi:10.1002/jcb.25422 · Cited by 25 · see citing works
Metagenomic and metatranscriptomic profiling of Lactobacillus casei Zhang in the human gut
Wang, J., Zhang, J., Liu, W., Zhang, H., & Sun, Z. (2021) · npj Biofilms and Microbiomes · doi:10.1038/s41522-021-00227-2 · Cited by 25 · see citing works
Alan, Y., Savcı, A., Koçpınar, E. F., et al. (2022) · Archives of Microbiology · doi:10.1007/s00203-022-03180-6 · Cited by 25 · see citing works
Juntarachot, N., Sunpaweravong, S., Kaewdech, A., et al. (2023) · Journal of Taibah University Medical Sciences · doi:10.1016/j.jtumed.2023.02.017 · Cited by 24 · see citing works
Sadeghirashed, S., Kazemi, F., Taheri, S., Ebrahimi, M. T., & Arasteh, J. (2021) · Metabolic Brain Disease · doi:10.1007/s11011-021-00857-7 · Cited by 23 · see citing works
Xing, T., Bian, X., Ma, C., Yang, Y., Liu, X., Wang, Y., Fan, J., & Zhang, N. (2023) · Food & Function · doi:10.1039/d2fo03200g · Cited by 23 · see citing works
Jeong, J., & Kim, S. (2024) · Food Science and Biotechnology · doi:10.1007/s10068-024-01744-4 · Cited by 23 · see citing works
A Global Review of Geographical Diversity of Kefir Microbiome
Ströher, J. A., Oliveira, W. D. C., de Freitas, A. S., Salazar, M. M., da Silva, L. D. F. F., Bresciani, L., Flôres, S. H., & Malheiros, P. D. S. (2025) · Fermentation · doi:10.3390/fermentation11030150 · Cited by 23 · see citing works
Shehata, H. R., Hassane, B., & Newmaster, S. G. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1076631 · Cited by 22 · see citing works
Shi, W., Naumovski, N., Ajlouni, S., Ayyash, M. M., Silva, R., Balthazar, C. F., Esmerino, E. A., Freitas, M. Q., Silva, M. C., Sant’Ana, A. S., et al. (2023) · International Journal of Dairy Technology · doi:10.1111/1471-0307.12976 · Cited by 22 · see citing works
Yang, S., Bai, M., Liu, W., Li, W., Zhong, Z., Kwok, L., Dong, G., & Sun, Z. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2569-7 · Cited by 22 · see citing works
Karim, M. R., Morshed, M. N., Iqbal, S., Mohammad, S., Mathiyalagan, R., Yang, D., Kim, Y. J., Song, J., & Yang, D. U. (2023) · Biomolecules · doi:10.3390/biom13111678 · Cited by 21 · see citing works
Özçelik, F., Dündar, M. S., Yildirim, A. B., HENEHAN, G. T. M., Vicente, O., Sánchez-Alcázar, J. A., Gökçe, N., Yildirim, D. T., Bingol, N. N., Plaseska‐Karanfilska, D., et al. (2024) · Functional & Integrative Genomics · doi:10.1007/s10142-024-01417-9 · Cited by 21 · see citing works
Sharma, V., Mobeen, F., & Prakash, T. (2018) · Genes · doi:10.3390/genes9100477 · Cited by 20 · see citing works
Cai, X., Wen, J.-S., Long, H., et al. (2022) · Aquaculture Reports · doi:10.1016/j.aqrep.2022.101299 · Cited by 20 · see citing works
Nakamura, Y., Suzuki, S., Murakami, S., Nishimoto, Y., Higashi, K., Watarai, N., Umetsu, J., Ishii, C., Ito, Y., Mori, Y., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.10.026 · Cited by 20 · see citing works
Nguyen, T. H., Kim, J.-S., Kwon, H., et al. (2023) · Fermentation · doi:10.3390/fermentation9020187 · Cited by 20 · see citing works
Noda, M., Danshiitsoodol, N., Sakaguchi, T., Kanno, K., & Sugiyama, M. (2021) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.b21-00517 · Cited by 19 · see citing works
Sydor, S., Dandyk, C., Schwerdt, J., Manka, P. P., Benndorf, D., Lehmann, T., Schallert, K., Wolf, M., Reichl, U., Canbay, A. E., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23168841 · Cited by 19 · see citing works
Al-Adham, I. S. I., Agha, A. S. A. A., Al‐Akayleh, F., Al-Remawi, M. M., Jaber, N., Al-Manasur, M. A., & Collier, P. J. (2025) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-025-10465-x · Cited by 19 · see citing works
Wu, H., Shum, T., & Chiou, J. A. (2021) · Microorganisms · doi:10.3390/microorganisms9122544 · Cited by 18 · see citing works
The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management
Vulpoi, R. A., Luca, M., Ciobanu, A., OLTEANU, A. V., Bărboi, O., Iov, D., Nichita, L., Ciortescu, I., Prelipcean, C. C., Ștefănescu, G., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13213336 · Cited by 18 · see citing works
Walczak‐Skierska, J., Ludwiczak, A., Sibińska, E., & Pomastowski, P. (2024) · ACS Omega · doi:10.1021/acsomega.4c03778 · Cited by 18 · see citing works
Toward a systematic design of smart probiotics
Choudhary, R., & Mahadevan, R. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.05.003 · Cited by 17 · see citing works
Bergamini, C. M., Bianchi, N., Giaccone, V., Catellani, P., Alberghini, L., Stella, A., Biffani, S., Yaddehige, S. K., Bobbo, T., & Taccioli, C. (2022) · Biology · doi:10.3390/biology11071024 · Cited by 17 · see citing works
Robotic Process Automation in Healthcare
Dhatterwal, J. S., Kaswan, K. S., & Kumar, N. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-8296-5_7 · Cited by 17 · see citing works
Zhang, G., He, M., Xiao, L., et al. (2023) · Journal of Dairy Science · doi:10.3168/jds.2023-23591 · Cited by 17 · see citing works
Unlocking human immune system complexity through AI
Berson, E., Chung, P., Espinosa, C. A., Montine, T. J., & Aghaeepour, N. (2024) · Nature Methods · doi:10.1038/s41592-024-02351-1 · Cited by 17 · see citing works
Pensupa, N., Treebuppachartsakul, T., & Pechprasarn, S. (2023) · Fermentation · doi:10.3390/fermentation9030239 · Cited by 16 · see citing works
Artificially engineered bacteria to treat gastrointestinal disease and cancer
Liu, Y., Yu, W., Wang, Q., Cao, Z., & Li, J. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103667 · Cited by 16 · see citing works
Computational prediction of new therapeutic effects of probiotics
Sulaimany, S., Farahmandi, K., & Mafakheri, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-62796-4 · Cited by 16 · see citing works
In vitro and in vivo lipidomics as a tool for probiotics evaluation
Schifano, E., Cicalini, I., Pieragostino, D., et al. (2020) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-020-10864-w · Cited by 14 · see citing works
Shehata, M. G., Masry, S. H. D., El-Aziz, N. M. A., Ridouane, F. L., Mirza, S. B., & Sohaimy, S. A. E. (2024) · Annals of Agricultural Science Cairo · doi:10.1016/j.aoas.2024.06.001 · Cited by 14 · see citing works
Zhai, Y., Kim, M., Fan, P. F., Rajeev, S., Kim, S. A., Driver, J., Galvão, K. N., Boucher, C. A., & Jeong, K. C. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1507537 · Cited by 14 · see citing works
Peng, Y., Chen, R., Zhang, Z., Jin, R., Xie, T., Liu, X., Chai, J., Howe, S. M., Zhao, J., Li, Y., et al. (2023) · Fermentation · doi:10.3390/fermentation9050417 · Cited by 13 · see citing works
ProbML: A Machine Learning‐Based Genome Classifier for Identifying Probiotic Organisms
Krishnan, A. O., Mudgal, L. N., Soni, V., & Prakash, T. (2025) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.70025 · Cited by 13 · see citing works
Cho, H., Jo, M., Oh, H., Lee, Y., & Park, Y. (2023) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2023.109326 · Cited by 12 · see citing works
Diffuse interface model of the freeze-drying process of individually frozen products
Bobba, S., Harguindeguy, M., Colucci, D., et al. (2020) · Drying Technology · doi:10.1080/07373937.2019.1710711 · Cited by 11 · see citing works
Response surface methodology to tune artificial neural network hyper‐parameters
Keser, S. B., & Şahin, Y. B. (2021) · Expert Systems · doi:10.1111/exsy.12792 · Cited by 11 · see citing works
Yang, F., Zhu, W., Edirisuriya, P., Ai, Q., Nie, K., Ji, X., & Zhou, K. (2022) · European Journal of Nutrition · doi:10.1007/s00394-022-02840-z · Cited by 11 · see citing works
Yusuff, A. S., Ishola, N. B., & Gbadamosi, A. O. (2023) · ACS Omega · doi:10.1021/acsomega.2c08117 · Cited by 11 · see citing works
Zhang, H., Zhang, S., Chen, L., Xu, R. P., & Zhu, J. (2024) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2024.106975 · Cited by 11 · see citing works
Vergara, S. C., Pendón, M. D., Bengoa, A. A., et al. (2024) · Journal of Fungi · doi:10.3390/jof10120878 · Cited by 9 · see citing works
Computer Vision-Based Image Analysis of Bacteria
Danielsen, J., & Nordenfelt, P. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6673-8_10 · Cited by 8 · see citing works
Xue, W., Liu, C., Liu, Y., Ding, H., An, C., Zhang, S., Ma, S., & Zhang, Q. (2024) · Fermentation · doi:10.3390/fermentation10020087 · Cited by 8 · see citing works
Kusmiati, K., Nurkanto, A., Fanani, A., Nurcahyanto, D. A., Mamangkey, J., Marissa, S., Pasaribu, K. M., Afiati, F., Agustini, N. W. S., Ahmad, R. Z., et al. (2025) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2025.101146 · Cited by 8 · see citing works
Rueangsri, N., Roytrakul, S., Muangnoi, C., Tongkhao, K., Sae‐tan, S., Treesuwan, K., & Sirivarasai, J. (2025) · Foods · doi:10.3390/foods14071148 · Cited by 8 · see citing works
Zhu, H. D., Jin, D., Fu, N., et al. (2022) · Powder Technology · doi:10.1016/j.powtec.2022.118042 · Cited by 7 · see citing works
Abdelwahab, S. I., Taha, M. M. E., Jerah, A. A., et al. (2024) · Cellular and Molecular Biology · doi:10.14715/cmb/2024.70.10.24 · Cited by 7 · see citing works
Li, Y., Liu, X., Cao, Z., & Wu, J. (2024) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-024-10418-w · Cited by 7 · see citing works
HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses
Wu, Z., Feng, C., Hu, Y.-S., Zhou, Y., Li, S.-D., Zhang, S.-L., Hu, Y.-M., Chen, Y.-H., Chao, H.-Y., Ni, Q.-Q., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02781-0 · Cited by 6 · see citing works
Kavak, A. E., Zent, İ., Özdemir, A., & Dertli̇, E. (2024) · Preparative Biochemistry & Biotechnology · doi:10.1080/10826068.2024.2312450 · Cited by 6 · see citing works
Self-citationQuantum AI in Healthcare : Revolutionizing Diagnosis, Treatment and Drug Discovery
Banerjee, N., & Chatterjee, K. (2024) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst2411351 · Cited by 6 · see citing works
Trackability of proteins from probiotic Bifidobacterium spp. in the gut using metaproteomics
Hendrickx, D. M., An, R., Boeren, S. A., Mutte, S. K., the PRESTO study team, Wopereis, H., & Belzer, C. (2023) · Beneficial Microbes · doi:10.1163/18762891-20220137 · Cited by 5 · see citing works
Martinović, A., Martinović, I., Mora, D., & Arioli, S. (2024) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-024-10400-6 · Cited by 5 · see citing works
AI Technology for Anti-Aging: an Overview
Huang, A., Huo, Y., Zhong, Y., & Yang, W. (2023) · International Conference on Intelligent Supercomputing and BioPharma (ISBP) · doi:10.1109/isbp57705.2023.10061311 · Cited by 4 · see citing works
Ding, J., Wang, B., Liu, Q., et al. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2024.130534 · Cited by 4 · see citing works
Das, D., Roy, A. D., Dasgupta, S., & Roy, R. D. (2024) · Indian Journal of Microbiology Research · doi:10.18231/j.ijmr.2024.027 · Cited by 4 · see citing works
Abdi, A., Gatri, E., Guilbaud, J., et al. (2025) · Fermentation · doi:10.3390/fermentation11040169 · Cited by 4 · see citing works
Dossou, B. F. P., Liu, D., Xu, J., Jain, M. J., van der Sloot, A. M., Palou, R., Tyers, M. D., & Bengio, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-2239137/v1 · Cited by 3 · see citing works
Kobayashi, Y., Uchida, T., Inoue, T., Iwasaki, Y., Ito, R., Saito, K., Akiyama, H., Tsuda, K., & Yoshida, K. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.697 · Cited by 3 · see citing works
Myers, B. K., Lamichhane, A., Kvitko, B. H., & Dutta, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.03.611110 · Cited by 2 · see citing works
Ajibade, B. O., Ajayeoba, T. A., Sabiu, S., Moiseenko, K. V., Mbona, S. V., Cason, E. D., Федорова, Т. В., & Ijabadeniyi, O. A. (2024) · Fermentation · doi:10.3390/fermentation11010006 · Cited by 2 · see citing works
MATHEMATICAL MODEL STUDY TO OPTIMIZE THE FREEZE DRYING PROCESS FOR PRODUCTION OF DRIED YOGURT
(2024) · Carpathian Journal of Food Science and Technology · doi:10.34302/crpjfst/2024.16.4.12 · Cited by 2 · see citing works
Investigation Into the Impact of Varying Inulin Levels on the Survival of Probiotic Microorganisms
Chutrtong, J., Kularbphettong, K., & Sirichokworrakit, S. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00102 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
“The growth of AI capabilities is fueling new fields, such as probiogenomics, where computational models are used to predict how a probiotic’s genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects [164,169].”
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
- Asar, R., Erenler, S., Devecioglu, D., Ispirli, H., Karbancioglu-Guler, F., Ozturk, H. I., & Dertli, E.
- Affiliations
- Yıldız Technical University (TR)
- Istanbul Technical University (TR)
- Food Engineering Department, Chemical and Metallurgical Engineering Faculty, İstanbul Technical University, Istanbul 34437, Türkiye
- Food Engineering Department, Engineering Faculty, Bayburt University, Bayburt 69100, Türkiye
- Food Engineering Department, Chemical and Metallurgical Engineering Faculty, Yildiz Technical University, Istanbul 34349, Türkiye
- Corresponding author
- Dertli, Enes
- Venue
- Fermentation
- Publisher
- MDPI AG
- Published
- 2025-05-05
- Volume
- 11
- Issue
- 5
- Pages
- 259
- ISSN
- 2311-5637 (ISSN-L)
- OpenAlex
- W4410081544
- Semantic Scholar
- 4df6eeca720c01ceac30c0b47a98f894357b843b
- Cited by
- 37
- References
- 199
- Citations by year
- 2026: 24 · 2025: 13
- FWCI
- 17.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Fermentation (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@Bayburt (submittedVersion, other-oa)
- Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
- 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
- Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Animal Nutrition and Physiology
- Keywords
- probiotics, artificial intelligence, probiotic viability, microbiota modulation, metabolite production, immune interactions, gut health, anticancer activity, anti-aging effects, next-generation probiotics
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