Artificial intelligence-driven strategies to enhance the application of lactic acid bacteria as functional probiotics: Health promotion and optimization for industrial applications
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High variability of biological datasets on different sequence determination platforms causes problems with reliability of source data, underestimating the accuracy of AI models.
“For example, the high variability of biological datasets on different platforms for sequence determination causes problems with the reliability of the source data, which undoubtedly underestimates the accuracy of the AI model [154,172].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [172]
Cites
Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,152 · see citing works
Metabolism Characteristics of Lactic Acid Bacteria and the Expanding Applications in Food Industry
Wang, Y., Wu, J., Lv, M., Shao, Z., Hungwe, M., Wang, J., Bai, X., Xie, J., Wang, Y., & Geng, W. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.612285 · Cited by 1,001 · see citing works
Lactic Acid Bacteria: Food Safety and Human Health Applications
Ayivi, R. D., Gyawali, R., Krastanov, A. I., Aljaloud, S. O., Worku, M., Tahergorabi, R., da Silva, R. C., & Ibrahim, S. A. (2020) · Dairy · doi:10.3390/dairy1030015 · Cited by 408 · see citing works
Molla, G., & Bitew, M. (2024) · Biomedicines · doi:10.3390/biomedicines12122750 · Cited by 277 · see citing works
Abouelela, M. E., & Helmy, Y. A. (2024) · Microorganisms · doi:10.3390/microorganisms12030430 · Cited by 259 · see citing works
Hakim, B. N. A., Xuan, N. J., & Oslan, S. N. H. (2023) · Foods · doi:10.3390/foods12152850 · Cited by 245 · see citing works
Review article: the future of microbiome‐based therapeutics
Gulliver, E. L., Young, R. B., Chonwerawong, M., D’Adamo, G. L., Thomason, T., Widdop, J. T., Rutten, E. L., Marcelino, V. R., Bryant, R. V., Costello, S. P., et al. (2022) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.17049 · Cited by 192 · see citing works
Abedin, M. M., Chourasia, R., Phukon, L. C., Sarkar, P., Ray, R. C., Singh, S. P., & Kumar, A. (2023) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2023.2227896 · Cited by 179 · 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 138 · see citing works
Janković, S. D., & Curović, D. (2023) · Sustainability · doi:10.3390/su152115208 · Cited by 124 · 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
Bio-functional properties of probiotic Lactobacillus: current applications and research perspectives
Minj, J., Chandra, P., Paul, C., & Sharma, R. K. (2020) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2020.1774496 · Cited by 110 · see citing works
Shukla, V., Singh, S., Verma, S., Verma, S., Rizvi, A. A., & Abbas, M. (2024) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2024.08.005 · Cited by 110 · see citing works
Fonseca, F., Pénicaud, C., Tymczyszyn, E. E., Gómez‐Zavaglia, A., & Passot, S. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-10002-1 · Cited by 105 · see citing works
Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions
Ganatra, H. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14030807 · Cited by 93 · 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
Recent biotechnological trends in lactic acid bacterial fermentation for food processing industries
Raj, T., Chandrasekhar, K., Kumar, A. N., & Kim, S. (2021) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-021-00044-w · Cited by 89 · see citing works
XAI-Based Clinical Decision Support Systems: A Systematic Review
Kim, S. Y., Kim, D. H., Kim, M., Ko, H.-J., & Jeong, O. (2024) · Applied Sciences · doi:10.3390/app14156638 · Cited by 84 · see citing works
Han, G., & Vaishnava, S. (2023) · Experimental & Molecular Medicine · doi:10.1038/s12276-023-01120-y · Cited by 77 · 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
Zhang, D., Zhang, J., Kalimuthu, S., Liu, J., Song, Z.-M., He, B.-B., Cai, P., Zhong, Z., Feng, C., Neelakantan, P., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01540-y · Cited by 72 · 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
Kim, K., Kim, J.-W., Kim, S.-I., Kim, S., Nguyen, T. H., & Kang, C.-H. (2021) · Microorganisms · doi:10.3390/microorganisms9091971 · Cited by 66 · see citing works
Self-citationZhong, X., Zhao, Y., Huang, L. J., Liu, J., Wang, K., Gao, X., Zhao, X., & Wang, X. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1140498 · Cited by 66 · see citing works
Lactic Acid Bacteria in Sustainable Food Production
Rachwał, K., & Gustaw, K. (2024) · Sustainability · doi:10.3390/su16083362 · 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
Lian, X., Yang, S., Li, H., Fu, C., & Zhang, Z. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00074 · Cited by 61 · see citing works
Alabbadi, A., & Bajaber, F. (2025) · Sensors · doi:10.3390/s25030847 · Cited by 58 · 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
Ayed, L., M’hir, S., Nuzzolese, D., Cagno, R. D., & Filannino, P. (2024) · Foods · doi:10.3390/foods13101538 · Cited by 54 · see citing works
Huang, Y., Xiaojun, L., Yu, S., Chen, R., & Chen, W.-Z. (2022) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00314 · Cited by 50 · see citing works
Levit, R., Cortes‐Perez, N. G., de LeBlanc, A. D. M., Loiseau, J., Aucouturier, A., Langella, P., LeBlanc, J. G., & Bermúdez‐Humarán, L. G. (2022) · Gut Microbes · doi:10.1080/19490976.2022.2110821 · Cited by 49 · see citing works
Host-microbiome protein-protein interactions capture disease-relevant pathways
Zhou, H., Beltrán, J. F., & Brito, I. (2022) · Genome biology · doi:10.1186/s13059-022-02643-9 · Cited by 46 · see citing works
Sheng, S., Fu, Y., Pan, N., Zhang, H. C., Xiu, L., Liang, Y., Liu, Y., Liu, B., Ma, C., Du, R., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1015270 · Cited by 45 · see citing works
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
Data-driven prediction of colonization outcomes for complex microbial communities
Wu, L., Wang, X.-W., Tao, Z., Wang, T., Zuo, W., Zeng, Y., Liu, Y.-Y., & Dai, L. (2024) · Nature Communications · doi:10.1038/s41467-024-46766-y · Cited by 44 · see citing works
Jafarinasab, T., Khaleghi, M., Farsinejad, A., & Khorrami, S. (2021) · Biotechnology Reports · doi:10.1016/j.btre.2021.e00593 · Cited by 43 · 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
Zafar, H., Ain, N. U., Alshammari, A., Alghamdi, S., Raja, H., Ali, A., Siddique, A., Tahir, S. D., Akbar, S., Arif, M., et al. (2022) · Nutrients · doi:10.3390/nu14081654 · Cited by 41 · see citing works
Sah, A. K., Elshaikh, R. H., Shalabi, M. G., Abbas, A. M., Prabhakar, P. K., Babker, A. M. A., Choudhary, R. K., Gaur, V., Choudhary, A. S., & Agarwal, S. (2025) · Life · doi:10.3390/life15050745 · Cited by 41 · see citing works
Bao, W., He, Y., Yu, J., Liu, M., Yang, X., Ta, N., Zhang, E., & Liang, C. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.921875 · Cited by 40 · see citing works
Randomized Explainable Machine Learning Models for Efficient Medical Diagnosis
Muhammad, D., Ahmed, I., Ahmad, M. O., & Bendechache, M. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3491593 · Cited by 39 · see citing works
Advances in the isolation, cultivation, and identification of gut microbes
Xu, M., Pan, F., Peng, L., & Yang, Y. (2024) · Military Medical Research · doi:10.1186/s40779-024-00534-7 · Cited by 38 · see citing works
Buddhasiri, S., Sukjoi, C., Kaewsakhorn, T., Nambunmee, K., Nakphaichit, M., Nitisinprasert, S., & Thiennimitr, P. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.716761 · Cited by 37 · see citing works
Advanced probiotics: bioengineering and their therapeutic application
Parvin, T., & Sadras, S. R. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-09309-8 · Cited by 37 · see citing works
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
Speckmann, B., Ehring, E., Hu, J., & Rodriguez‐Mateos, A. (2024) · Gut Microbes · doi:10.1080/19490976.2024.2305716 · 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
Zampieri, G., Efthimiou, G., & Angione, C. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.01.008 · Cited by 35 · see citing works
Zhang, Y., Liu, L.-H., Xu, B., Zhang, Z., Yang, M., He, Y.-Y., Chen, J.-J., Zhang, Y., Hu, Y.-C., Chen, X., et al. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.05.003 · Cited by 35 · see citing works
Xie, Z., McAuliffe, O. E., Jin, Y., & Miller, M. J. (2024) · Journal of Dairy Science · doi:10.3168/jds.2024-24989 · Cited by 35 · see citing works
AI-Driven Telerehabilitation: Benefits and Challenges of a Transformative Healthcare Approach
Calabrò, R. S., & Mojdehdehbaher, S. (2025) · AI · doi:10.3390/ai6030062 · Cited by 35 · 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
Liu, Y., Hsu, C.-Y., Yang, Y.-C., Huang, C., & Chen, C.-C. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad153 · Cited by 33 · see citing works
Peter, S. B., Qiao, Z., Godspower, H. N., Ajeje, S. B., Xu, M., Zhang, X., Yang, T., & Rao, Z. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.802031 · Cited by 32 · see citing works
Ladda, B., Jantararussamee, C., Pradidarcheep, W., Kasorn, A., Matsathit, U., & Taweechotipatr, M. (2023) · Nutrients · doi:10.3390/nu15061388 · Cited by 32 · see citing works
Maione, A., Imparato, M., Buonanno, A., Salvatore, M. M., Carraturo, F., de Alteriis, E., Guida, M., & Galdiero, E. (2024) · Foods · doi:10.3390/foods13071013 · Cited by 32 · see citing works
Koduru, L., Lakshmanan, M., Lee, Y. Q., Ho, P. L., Lim, P. Y., Ler, W. X., Ng, S. K., Kim, D., Park, D., Banu, M., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111735 · Cited by 31 · see citing works
Self-citationFan, X., Ling, N., Liu, C., et al. (2023) · Ultrasonics Sonochemistry · doi:10.1016/j.ultsonch.2023.106698 · Cited by 31 · 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
Automatic Assessment of Privacy Policies under the GDPR
Sánchez, D., Viejo, A., & Batet, M. (2021) · Applied Sciences · doi:10.3390/app11041762 · Cited by 27 · 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
Srivastava, U., Nataraj, B. H., Kumari, M., Kadyan, S., Puniya, A. K., Behare, P. V., & Nagpal, R. (2022) · Peptides · doi:10.1016/j.peptides.2022.170843 · Cited by 25 · see citing works
Probio-Ichnos: A Database of Microorganisms with In Vitro Probiotic Properties
Tsifintaris, M., Kiousi, D. E., Repanas, P., Kamarinou, C. S., Kavakiotis, I., & Galanis, Α. (2024) · Microorganisms · doi:10.3390/microorganisms12101955 · Cited by 24 · see citing works
Cheng, H. T., Yen, G., Huang, S., Chen, S., & Hsu, C. (2022) · Journal of Food and Drug Analysis · doi:10.38212/2224-6614.3396 · Cited by 23 · see citing works
Steps toward a digital twin for functional food production with increased health benefits
Sørensen, H. M., Cunningham, D., Balakrishnan, R., Maye, S., Macleod, G. H. B., Brabazon, D., Loscher, C. E., & Freeland, B. H. (2023) · Current Research in Food Science · doi:10.1016/j.crfs.2023.100593 · Cited by 23 · see citing works
Li, L., Wu, L., Jiang, T., Liang, T., Yang, L., Li, Y., Gao, H., Zhang, J., Xie, X., & Wu, Q. (2023) · Nutrients · doi:10.3390/nu15184002 · Cited by 22 · see citing works
Precision probiotics supplement strategy in aging population based on gut microbiome composition
Chuang, Y., Fan, K.-C., Su, Y.-Y., Wu, M.-F., Chiu, Y., Liu, Y., & Lin, C.-C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae351 · Cited by 22 · see citing works
Wang, K., Li, X., Xu, X., Chang, J., Sun, M., Huang, K., Huang, S., Jiang, H., Wang, H., Tan, M., et al. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.164598 · Cited by 22 · see citing works
Emerging applications of artificial intelligence in pathogen genomics
Suster, C. J. E., Pham, D., Kok, J., & Sintchenko, V. (2024) · Frontiers in Bacteriology · doi:10.3389/fbrio.2024.1326958 · Cited by 21 · see citing works
Current Regulatory Issues for the Use of Probiotics
Szajewska, H., & Vinderola, G. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-58572-2_12 · Cited by 21 · see citing works
Laterza, L., Putignani, L., Settanni, C. R., Petito, V., Varca, S., Maio, F. D., Macari, G., Guarrasi, V., Gremese, E., Tolusso, B., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076623 · Cited by 20 · see citing works
Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods
Liu, X., Li, H., Ma, C.-Y., Shi, T., Wang, T., Yan, D., Tang, H., Lin, H., & Deng, K. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae004 · Cited by 19 · see citing works
Parlindungan, E., & Jones, O. A. H. (2023) · Metabolomics · doi:10.1007/s11306-023-02062-2 · Cited by 18 · 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 18 · see citing works
Ye, Z., Guo, T., Tang, C., Yuan, Y., Zhao, Y. D., Lu, J., Lin, J., & Guo, L. (2020) · LWT · doi:10.1016/j.lwt.2020.110563 · 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
Miao, X., Jiang, Y., Kong, D., Wu, Z., Liu, H., Ye, X., & Gong, W. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.00794-22 · Cited by 16 · see citing works
Ismael, M., Gu, Y., Cui, Y., Wang, T., Yue, F., Qin, Y., & Lü, X. (2022) · Foods · doi:10.3390/foods11233766 · Cited by 16 · see citing works
Ren, Y. N., Zheng, Y., Wang, X., Qu, S., Sun, L., Song, C., Ding, J. J., Ji, Y., Wang, G., Zhu, P., et al. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1361180 · Cited by 16 · see citing works
Román, L., Melis-Arcos, F., Pröschle, T., Saa, P. A., & Garrido, D. A. (2024) · mSystems · doi:10.1128/msystems.00715-23 · Cited by 15 · see citing works
Silva, C., Hernández, S., Saa, P. A., Pérez‐Rueda, E., Garrido, D. A., & Martín, A. J. M. (2025) · PeerJ · doi:10.7717/peerj.19296 · Cited by 15 · see citing works
Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis
Karlsen, S. T., Vesth, T., Øregaard, G., Poulsen, V. K., Lund, O., Henderson, G., & Bælum, J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246287 · 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
D’ambrosio, S., Dabous, A., Sadiq, S., et al. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1379574 · Cited by 13 · see citing works
Meng, J., Dong, P.-P., Zhu, M.-X., et al. (2024) · Food Bioscience · doi:10.1016/j.fbio.2024.104786 · 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
HIPAA: A Demand to Modernize Health Legislation
Sadri, M. (2024) · The undergraduate law review at UC San Diego. · doi:10.5070/lr3.21252 · Cited by 12 · see citing works
Liu, S., Zhao, L., Li, M., et al. (2024) · Food Research International · doi:10.1016/j.foodres.2024.115249 · Cited by 12 · see citing works
Potential Antiviral Activity of Lactiplantibacillus plantarum KAU007 against Influenza Virus H1N1
Rather, I. A., Kamli, M. R., Sabir, J. S. M., et al. (2022) · Vaccines · doi:10.3390/vaccines10030456 · Cited by 10 · see citing works
Dai, J., Li, W., & Dong, G. (2024) · Foods · doi:10.3390/foods13131958 · Cited by 10 · see citing works
Dong, Y., Han, M., Qi, Y., Wu, Y., Zhou, Z., Jiang, D., & Gai, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1469885 · Cited by 10 · see citing works
Active induction: a promising strategy for enhancing the bioactivity of lactic acid bacteria
Chen, S., Suo, K., Kang, Q., Zhu, J., Shi, Y., Yi, J., & Lu, J. (2025) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2025.2479069 · Cited by 9 · see citing works
Yang, Z., Zhu, J., Lu, W., Tian, F., Zhang, H., & Chen, W. (2024) · Food Bioscience · doi:10.1016/j.fbio.2024.104541 · Cited by 8 · see citing works
Zhong, B., Liang, W. Y., Zhao, Y., Li, F., Zhao, Z., Gao, Y., Yang, G., & Li, S. (2024) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-024-10368-3 · Cited by 8 · 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
González, L. L., Arias-Serrano, I., Villalba-Meneses, F., Navas, P., & Cruz-Varela, J. (2024) · F1000Research · doi:10.12688/f1000research.154432.1 · Cited by 6 · see citing works
Kiouri, D. P., Batsis, G., Mavromoustakos, T., Giuliani, A., & Chasapis, C. T. (2025) · BioTech · doi:10.3390/biotech14010013 · Cited by 4 · see citing works
Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions
Kiouri, D. P., Batsis, G. C., & Chasapis, C. T. (2025) · Proteomes · doi:10.3390/proteomes13010010 · Cited by 3 · see citing works
Jafari, M., Nazari, R. R., Rezaei, M., et al. (2025) · Food Safety and Risk · doi:10.1186/s40550-025-00114-0 · Cited by 3 · see citing works
Xie, Y., Wang, S., Tian, Y. P., et al. (2025) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2025.107972 · Cited by 2 · see citing works
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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
“For example, the high variability of biological datasets on different platforms for sequence determination causes problems with the reliability of the source data, which undoubtedly underestimates the accuracy of the AI model [154,172].”
Gwak, Y.-S., Suh, H.-Y., Yoon, H.-R., Kim, S., Bae, C.-I., & Kim, M.-J. (2026) · Journal of Microbiology and Biotechnology · doi:10.4014/jmb.2606.06012
- Authors
- 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., & Khan, F.
- Affiliations
- Marine Biodiversity Institute of Korea (KR)
- Pukyong National University (KR)
- Corresponding author
- Jo, Du-Min
- Khan, Fazlurrahman
- ORCID
- Venue
- Trends in Food Science & Technology
- Publisher
- Elsevier BV
- Published
- 2025-11
- Volume
- 165
- Pages
- 105309
- ISSN
- 0924-2244 (ISSN-L)
- OpenAlex
- W4414143967
- Cited by
- 20
- References
- 102
- Citations by year
- 2026: 17 · 2025: 3
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Trends in Food Science & Technology (publishedVersion)
- Funders
- Korea Ministry of Education (RS-2023\u201300241461)
- National Research Foundation of Korea
- National Marine Biodiversity Institute of Korea (2025M00500)
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- lactic acid bacteria, probiotics, artificial intelligence, machine learning, deep learning, gut microbiota modulation, immune regulation, antimicrobial peptides, short-chain fatty acids, metabolic modeling, personalized health interventions, fermentation optimization, probiotic formulation, quality control
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