909
Views
0
CrossRef citations to date
0
Altmetric
Review Article

Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens

, , &
Article: 2324024 | Received 01 Feb 2024, Accepted 22 Feb 2024, Published online: 15 Mar 2024

References

  • Abd-Alhalem, S. M., Soliman, N. F., Eldin, S., Abd Elrahman, S. E., Ismail, N. A., El-Rabaie, E. S. M., & El-Samie, F. E. A. (2020). Bacterial classification with convolutional neural networks based on different data reduction layers. Nucleosides, Nucleotides & Nucleic Acids, 39(4), 493–16. https://doi.org/10.1080/15257770.2019.1645851
  • Adnan, M. N., & Islam, M. Z. (2016). Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm. Knowledge-Based Systems, 110, 86–97. https://doi.org/10.1016/j.knosys.2016.07.016
  • Aires de Sousa, M. (2017). Methicillin-resistant staphylococcus aureus among animals: Current overview. Clinical Microbiology and Infection, 23(6), 373–380. https://doi.org/10.1016/j.cmi.2016.11.002
  • Al-Aidaroos, K. M., Bakar, A. A., & Othman, Z. (2010, March). Naive Bayes variants in classification learning. In 2010 International Conference on Information Retrieval & Knowledge Management (CAMP) (pp. 276–281). IEEE. https://doi.org/10.1109/INFRKM.2010.5466902
  • Alcock, B. P., Raphenya, A. R., Lau, T. T., Tsang, K. K., Bouchard, M., Edalatmand, A., McArthur, A. G., Nguyen, A. L. V., Cheng, A. A., Liu, S., Min, S. Y., Miroshnichenko, A., Tran, H.-K., Werfalli, R. E., Nasir, J. A., Oloni, M., Speicher, D. J., Florescu, A. … Domselaar, G. V. (2020). CARD 2020: Antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Research, 48(D1), D517–D525. https://doi.org/10.1093/nar/gkz935
  • Ali, T., Ahmed, S., & Aslam, M. (2023). Artificial intelligence for antimicrobial resistance prediction: Challenges and opportunities towards practical implementation. Antibiotics, 12(3), 523. https://doi.org/10.3390/antibiotics12030523
  • Anahtar, M. N., Yang, J. H., Kanjilal, S., & McAdam, A. J. (2021). Applications of machine learning to the problem of antimicrobial resistance: An emerging model for translational research. Journal of Clinical Microbiology, 59(7), 10–1128. https://doi.org/10.1128/jcm.01260-20
  • Andreopoulos, W. B., Geller, A. M., Lucke, M., Balewski, J., Clum, A., Ivanova, N. N., & Levy, A. (2022). Deeplasmid: Deep learning accurately separates plasmids from bacterial chromosomes. Nucleic Acids Research, 50(3), e17. https://doi.org/10.1093/nar/gkab1115
  • Anjum, M. F., Marco-Jimenez, F., Duncan, D., Marín, C., Smith, R. P., & Evans, S. J. (2019). Livestock-associated methicillin-resistant staphylococcus aureus from animals and animal products in the UK. Frontiers in Microbiology, 10, 2136. https://doi.org/10.3389/fmicb.2019.02136
  • Annunziato, G. (2019). Strategies to overcome antimicrobial resistance (AMR) making use of non-essential target inhibitors: A review. International Journal of Molecular Sciences, 20(23), 5844. https://doi.org/10.3390/ijms20235844
  • Arredondo-Alonso, S., Rogers, M. R. C., Braat, J. C., Verschuuren, T. D., Top, J., Corander, J., Willems, R. J. L., & Schürch, A. C. (2018). Mlplasmids: A user-friendly tool to predict plasmid-and chromosome-derived sequences for single species. Microbial Genomics, 4(11). https://doi.org/10.1099/mgen.0.000224
  • Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360–3379. https://doi.org/10.1080/10494820.2021.1928235
  • Awad, M., Khanna, R., Awad, M., & Khanna, R. (2015). Support vector machines for classification. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, 39–66. https://doi.org/10.1007/978-1-4302-5990-9_3
  • Azmi, S. S., & Baliga, S. (2020). An Overview of Boosting Decision Tree Algorithms utilizing AdaBoost and XGBoost Boosting strategies. International Research Journal of Engineering & Technology, 7(5). 6867–6870
  • Baker, M., Zhang, X., Maciel-Guerra, A., Dong, Y., Wang, W., Hu, Y., Renney, D., Hu, Y., Liu, L., Li, H., Tong, Z., Zhang, M., Geng, Y., Zhao, L., Hao, Z., Senin, N., Chen, J., Peng, Z., Li, F., & Dottorini, T. (2023). Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China. Nature Food, 4(8), 707–720. https://doi.org/10.1038/s43016-023-00814-w
  • Bandyopadhyay, S., & Samanta, I. (2020). Antimicrobial resistance in agri-food chain and companion animals as a re-emerging menace in post-COVID epoch: Low-and middle-income countries perspective and mitigation strategies. Frontiers in Veterinary Science, 7, 620. https://doi.org/10.3389/fvets.2020.00620
  • Beceiro, A., Tomás, M., & Bou, G. (2013). Antimicrobial resistance and virulence: A successful or deleterious association in the bacterial world? Clinical Microbiology Reviews, 26(2), 185–230. https://doi.org/10.1128/cmr.00059-12
  • Bintsis, T. (2017). Foodborne pathogens. AIMS Microbiology, 3(3), 529. https://doi.org/10.3934/microbiol.2017.3.529
  • Blancquaert, D., Storozhenko, S., Loizeau, K., De Steur, H., De Brouwer, V., Viaene, J., Ravanel, S., Rébeillé, F., Lambert, W., & Van Der Straeten, D. (2010). Folates and folic acid: From fundamental research toward sustainable health. Critical Reviews in Plant Science, 29(1), 14–35. https://doi.org/10.1080/07352680903436283
  • Bonomo, R. A. (2017). β-lactamases: A focus on current challenges. Cold Spring Harbor Perspectives in Medicine, 7(1), a025239. https://doi.org/10.1101/cshperspect.a025239
  • Bonvegna, M., Tomassone, L., Christensen, H., & Olsen, J. E. (2022). Whole genome sequencing (WGS) analysis of virulence and AMR genes in extended-spectrum β-lactamase (ESBL)-producing Escherichia coli from animal and Environmental Samples in four Italian swine farms. Antibiotics, 11(12), 1774. https://doi.org/10.3390/antibiotics11121774
  • Boolchandani, M., D’Souza, A. W., & Dantas, G. (2019). Sequencing-based methods and resources to study antimicrobial resistance. Nature Reviews Genetics, 20(6), 356–370. https://doi.org/10.1038/s41576-019-0108-4
  • Breijyeh, Z., Jubeh, B., & Karaman, R. (2020). Resistance of gram-negative bacteria to current antibacterial agents and approaches to resolve it. Molecules, 25(6), 1340. https://doi.org/10.3390/molecules25061340
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Bush, K. (2010). Alarming β-lactamase-mediated resistance in multidrug-resistant Enterobacteriaceae. Current Opinion in Microbiology, 13(5), 558–564. https://doi.org/10.1016/j.mib.2010.09.006
  • Chauhan, V. K., Dahiya, K., & Sharma, A. (2019). Problem formulations and solvers in linear SVM: A review. Artificial Intelligence Review, 52(2), 803–855. https://doi.org/10.1007/s10462-018-9614-6
  • Chen, Q., Lan, C., Zhao, L., Wang, J., Chen, B., & Chen, Y. P. P. (2017). Recent advances in sequence assembly: Principles and applications. Briefings in Functional Genomics, 16(6), 361–378. https://doi.org/10.1093/bfgp/elx006
  • Chen, C., & Wu, F. (2021). Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) colonisation and infection among livestock workers and veterinarians: A systematic review and meta-analysis. Occupational and Environmental Medicine, 78(7), 530–540. https://doi.org/10.1136/oemed-2020-106418
  • Chin, F. Y., Leung, H. C., & Yiu, S. M. (2014). Sequence assembly using next generation sequencing data—challenges and solutions. Science China Life Sciences, 57(11), 1140–1148. https://doi.org/10.1007/s11427-014-4752-9
  • Chuang, Y. Y., & Huang, Y. C. (2015). Livestock-associated meticillin-resistant staphylococcus aureus in Asia: An emerging issue? International Journal of Antimicrobial Agents, 45(4), 334–340. https://doi.org/10.1016/j.ijantimicag.2014.12.007
  • Crespo-Piazuelo, D., & Lawlor, P. G. (2021). Livestock-associated methicillin-resistant staphylococcus aureus (LA-MRSA) prevalence in humans in close contact with animals and measures to reduce on-farm colonisation. Irish Veterinary Journal, 74(1), 1–12. https://doi.org/10.1186/s13620-021-00200-7
  • Davis, J. J., Wattam, A. R., Aziz, R. K., Brettin, T., Butler, R., Butler, R. M., Stevens, R., Conrad, N., Dickerman, A., Dietrich, E. M., Gabbard, J. L., Gerdes, S., Guard, A., Kenyon, R. W., Machi, D., Mao, C., Murphy-Olson, D., Nguyen, M. … Yoo, H. (2020). The PATRIC bioinformatics resource center: Expanding data and analysis capabilities. Nucleic Acids Research, 48(D1), D606–D612. https://doi.org/10.1093/nar/gkz943
  • De Boer, E., Zwartkruis-Nahuis, J. T. M., Wit, B., Huijsdens, X. W., De Neeling, A. J., Bosch, T., Van Oosterom, R. A. A., Vila, A., & Heuvelink, A. E. (2009). Prevalence of methicillin-resistant Staphylococcus aureus in meat. International Journal of Food Microbiology, 134(1–2), 52–56. https://doi.org/10.1016/j.ijfoodmicro.2008.12.007
  • Delcour, A. H. (2009). Outer membrane permeability and antibiotic resistance. Biochimica Et Biophysica Acta (BBA)-Proteins and Proteomics, 1794(5), 808–816. https://doi.org/10.1016/j.bbapap.2008.11.005
  • Dhanda, G., Acharya, Y., & Haldar, J. (2023). Antibiotic adjuvants: A versatile approach to combat antibiotic resistance. American Chemical Society Omega, 8(12), 10757–10783. https://doi.org/10.1021/acsomega.3c00312
  • Dieckmann, R., Hammerl, J. A., Hahmann, H., Wicke, A., Kleta, S., Dabrowski, P. W., Nitsche, A., Stämmler, M., Al Dahouk, S., & Lasch, P. (2016). Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. Faraday Discussions, 187, 353–375. https://doi.org/10.1039/C5FD00165J
  • Doyle, M. E. (2015). Multidrug-resistant pathogens in the food supply. Foodborne Pathogens and Disease, 12(4), 261–279. https://doi.org/10.1089/fpd.2014.1865
  • Du, T. Y. (2019). Dimensionality reduction techniques for visualizing morphometric data: Comparing principal component analysis to nonlinear methods. Evolutionary Biology, 46(1), 106–121. https://doi.org/10.1007/s11692-018-9464-9
  • Du, D., Wang-Kan, X., Neuberger, A., Van Veen, H. W., Pos, K. M., Piddock, L. J., & Luisi, B. F. (2018). Multidrug efflux pumps: Structure, function and regulation. Nature Reviews Microbiology, 16(9), 523–539. https://doi.org/10.1038/s41579-018-0048-6
  • Economou, V., & Gousia, P. (2015). Agriculture and food animals as a source of antimicrobial-resistant bacteria. Infection and Drug Resistance, 49–61. https://doi.org/10.2147/IDR.S55778
  • EFSA & ECDC. (2022). The European Union summary report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals and food in 2019-2020. European Food Safety Authority Journal, 20(3), e07209. https://doi.org/10.2903/j.efsa.2022.7209
  • Ehuwa, O., Jaiswal, A. K., & Jaiswal, S. (2021). Salmonella, food safety and food handling practices. Foods, 10(5), 907. https://doi.org/10.3390/foods10050907
  • El Bouchefry, K., & de Souza, R. S. (2020). Learning in big data: Introduction to machine learning. Knowledge Discovery in Big Data from Astronomy and Earth Observation, 225–249. https://doi.org/10.1016/B978-0-12-819154-5.00023-0
  • Esener, N., Maciel-Guerra, A., Giebel, K., Lea, D., Green, M. J., Bradley, A. J., Dottorini, T., & Holmes, M. (2021). Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of staphylococcus aureus in bovine mastitis. PLoS Computational Biology, 17(6), e1009108. https://doi.org/10.1371/journal.pcbi.1009108
  • Espinoza, N., Rojas, J., Pollett, S., Meza, R., Patino, L., Leiva, M., Camiña, M., Bernal, M., Reynolds, N. D., Maves, R., Tilley, D. H., Kasper, M., & Simons, M. P. (2020). Validation of the T86I mutation in the gyrA gene as a highly reliable real time PCR target to detect Fluoroquinolone-resistant Campylobacter jejuni. BMC Infectious Diseases, 20(1), 1–7. https://doi.org/10.1186/s12879-020-05202-4
  • Feldgarden, M., Brover, V., Haft, D. H., Prasad, A. B., Slotta, D. J., Tolstoy, I., Tyson, G. H., Zhao, S., Hsu, C.-H., McDermott, P. F., Tadesse, D. A., Morales, C., Simmons, M., Tillman, G., Wasilenko, J., Folster, J. P., & Klimke, W. (2019). Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. Antimicrobial Agents and Chemotherapy, 63(11), 10–1128. https://doi.org/10.1128/aac.00483-19
  • Feucherolles, M., Nennig, M., Becker, S. L., Martiny, D., Losch, S., Penny, C., Cauchie, H.-M., & Ragimbeau, C. (2022). Combination of MALDI-TOF mass spectrometry and machine learning for rapid antimicrobial resistance screening: The case of Campylobacter spp. Frontiers in Microbiology, 12, 804484. https://doi.org/10.3389/fmicb.2021.804484
  • Florensa, A. F., Kaas, R. S., Clausen, P. T. L. C., Aytan-Aktug, D., & Aarestrup, F. M. (2022). ResFinder–an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microbial Genomics, 8(1). https://doi.org/10.1099/mgen.0.000748
  • Frasao, B. D. S., Medeiros, V., Barbosa, A. V., De Aguiar, W. S., Dos Santos, F. F., Abreu, D. L. D. C., De Aquino, M. H. C., & de Aquino, M. H. C. (2015). Detection of fluoroquinolone resistance by mutation in gyrA gene of Campylobacter spp. isolates from broiler and laying (Gallus gallus domesticus) hens, from Rio de Janeiro State, Brazil. Ciencia Rural, 45(11), 2013–2018. https://doi.org/10.1590/0103-8478cr20141712
  • Freeland, G., Hettiarachchy, N., Atungulu, G. G., Apple, J., & Mukherjee, S. (2023). Strategies to combat antimicrobial resistance from farm to table. Food Reviews International, 39(1), 27–40. https://doi.org/10.1080/87559129.2021.1893744
  • Gan, T., Shu, G., Fu, H., Yan, Q., Zhang, W., Tang, H., Yin, L., Zhao, L., & Lin, J. (2021). Antimicrobial resistance and genotyping of Staphylococcus aureus obtained from food animals in Sichuan Province, China. BMC Veterinary Research, 17(1), 177. https://doi.org/10.1186/s12917-021-02884-z
  • Giedraitienė, A., Vitkauskienė, A., Naginienė, R., & Pavilonis, A. (2011). Antibiotic resistance mechanisms of clinically important bacteria. Medicina, 47(3), 19. https://doi.org/10.3390/medicina47030019
  • Golding, G. R., Bryden, L., Levett, P. N., McDonald, R. R., Wong, A., Wylie, J., Graham, M. R., Tyler, S., Van Domselaar, G., Simor, A. E., Gravel, D., & Mulvey, M. R. (2010). Livestock-associated methicillin-resistant Staphylococcus aureus sequence type 398 in humans, Canada. Emerging Infectious Diseases, 16(4), 587. https://doi.org/10.3201/eid1604.091435
  • Grudlewska-Buda, K., Bauza-Kaszewska, J., Wiktorczyk-Kapischke, N., Budzyńska, A., Gospodarek-Komkowska, E., & Skowron, K. (2023). Antibiotic resistance in selected emerging bacterial foodborne pathogens-an issue of concern? Antibiotics, 12(5), 880. https://doi.org/10.3390/antibiotics12050880
  • Gupta, S. K., Padmanabhan, B. R., Diene, S. M., Lopez-Rojas, R., Kempf, M., Landraud, L., & Rolain, J. M. (2014). ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrobial Agents and Chemotherapy, 58(1), 212–220. https://doi.org/10.1128/AAC.01310-13
  • Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072–1075. https://doi.org/10.1093/bioinformatics/btt086
  • Haag, A. F., Fitzgerald, J. R., Penadés, J. R., Fischetti, V. A., Novick, R. P., Ferretti, J. J., Portnoy, D. A., Braunstein, M., & Rood, J. I. (2019). Staphylococcus aureus in Animals. Microbiology Spectrum, 7(3), 10–1128. https://doi.org/10.1128/microbiolspec.gpp3-0060-2019
  • Hatje, K., & Kollmar, M. (2012). A phylogenetic analysis of the brassicales clade based on an alignment-free sequence comparison method. Frontiers in Plant Science, 3, 192. https://doi.org/10.3389/fpls.2012.00192
  • Hosain, M. Z., Kabir, S. L., & Kamal, M. M. (2021). Antimicrobial uses for livestock production in developing countries. Veterinary World, 14(1), 210. https://doi.org/10.14202/vetworld.2021.210-221
  • Hou, T. Y., Chiang-Ni, C., & Teng, S. H. (2019). Current status of MALDI-TOF mass spectrometry in clinical microbiology. Journal of Food and Drug Analysis, 27(2), 404–414. https://doi.org/10.1016/j.jfda.2019.01.001
  • Hudson, J. A., Frewer, L. J., Jones, G., Brereton, P. A., Whittingham, M. J., & Stewart, G. (2017). The agri-food chain and antimicrobial resistance: A review. Trends in Food Science & Technology, 69, 131–147. https://doi.org/10.1016/j.tifs.2017.09.007
  • Jia, B., Raphenya, A. R., Alcock, B., Waglechner, N., Guo, P., Tsang, K. K., McArthur, A. G., Dave, B. M., Pereira, S., Sharma, A. N., Doshi, S., Courtot, M., Lo, R., Williams, L. E., Frye, J. G., Elsayegh, T., Sardar, D., Westman, E. L., & Wright, G. D. (2017). CARD 2017: Expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Research, 45(D1), D566–573. https://doi.org/10.1093/nar/gkw1004
  • Kapoor, G., Saigal, S., & Elongavan, A. (2017). Action and resistance mechanisms of antibiotics: A guide for clinicians. Journal of Anaesthesiology, Clinical Pharmacology, 33(3), 300. https://doi.org/10.4103/joacp.JOACP_349_15
  • Khademi, F., Sahebkar, A., & Chaves Lopez, C. (2020). Prevalence of fluoroquinolone-resistant Campylobacter species in Iran: A systematic review and meta-analysis. International Journal of Microbiology, 2020, 1–14. https://doi.org/10.1155/2020/8868197
  • Khanna, T., Friendship, R., Dewey, C., & Weese, J. S. (2008). Methicillin resistant staphylococcus aureus colonization in pigs and pig farmers. Veterinary Microbiology, 128(3–4), 298–303. https://doi.org/10.1016/j.vetmic.2007.10.006
  • Khezri, A., Avershina, E., & Ahmad, R. (2021). Hybrid assembly provides improved resolution of plasmids, antimicrobial resistance genes, and virulence factors in Escherichia coli and Klebsiella pneumoniae clinical isolates. Microorganisms [Internet], 9(12), 2560. https://doi.org/10.3390/microorganisms9122560
  • Koch, B. J., Hungate, B. A., & Price, L. B. (2017). Food‐animal production and the spread of antibiotic resistance: The role of ecology. Frontiers in Ecology and the Environment, 15(6), 309–318. https://doi.org/10.1002/fee.1505
  • Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17. https://doi.org/10.1016/j.csbj.2014.11.005
  • Larsen, J., Raisen, C. L., Ba, X., Sadgrove, N. J., Padilla-González, G. F., Simmonds, M. S., Larsen, A. R., Kerschner, H., Apfalter, P., Hartl, R., Deplano, A., Vandendriessche, S., Černá Bolfíková, B., Hulva, P., Arendrup, M. C., Hare, R. K., Barnadas, C., Stegger, M. … Harrison, E. M. (2022). Emergence of methicillin resistance predates the clinical use of antibiotics. Nature, 602(7895), 135–141. https://doi.org/10.1038/s41586-021-04265-w
  • Liu, Z., Deng, D., Lu, H., Sun, J., Lv, L., Li, S., Peng, G., Ma, X., Li, J., Li, Z., Rong, T., & Wang, G. (2020). Evaluation of machine learning models for predicting antimicrobial resistance of actinobacillus pleuropneumoniae from whole genome sequences. Frontiers in Microbiology, 11, 48. https://doi.org/10.3389/fmicb.2020.00048
  • Livermore, D. M. (2000). Antibiotic resistance in staphylococci. International Journal of Antimicrobial Agents, 16, 3–10. https://doi.org/10.1016/S0924-8579(00)00299-5
  • Lu, W., Li, H., Qiu, H., Wang, L., Feng, J., & Fu, Y. V. (2023). Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning. Frontiers in Microbiology, 13, 1076965. https://doi.org/10.3389/fmicb.2022.1076965
  • Maguire, F., Rehman, M. A., Carrillo, C., Diarra, M. S., Beiko, R. G., & Gilbert, J. A. (2019). Identification of primary antimicrobial resistance drivers in agricultural nontyphoidal Salmonella enterica serovars by using machine learning. mSystems [Internet], 4(4), 10–1128. https://doi.org/10.1128/msystems.00211-19
  • Mann, A., Nehra, K., Rana, J. S., & Dahiya, T. (2021). Antibiotic resistance in agriculture: Perspectives on upcoming strategies to overcome upsurge in resistance. Current Research in Microbial Sciences, 2, 100030. https://doi.org/10.1016/j.crmicr.2021.100030
  • McArthur, A. G., Waglechner, N., Nizam, F., Yan, A., Azad, M. A., Baylay, A. J., Wright, G. D., Canova, M. J., De Pascale, G., Ejim, L., Kalan, L., King, A. M., Koteva, K., Morar, M., Mulvey, M. R., O’Brien, J. S., Pawlowski, A. C., Piddock, L. J. V. … Yu, T. (2013). The comprehensive antibiotic resistance database. Antimicrobial Agents and Chemotherapy, 57(7), 3348–3357. https://doi.org/10.1128/aac.00419-13
  • McEwen, S. A., & Fedorka-Cray, P. J. (2002). Antimicrobial use and resistance in animals. Clinical Infectious Diseases, 34(Supplement_3), S93–S106. https://doi.org/10.1086/340246
  • Medalla, F., Gu, W., Friedman, C. R., Judd, M., Folster, J., Griffin, P. M., & Hoekstra, R. M. (2021). Increased incidence of antimicrobial-resistant nontyphoidal Salmonella infections, United States, 2004-2016. Emerging Infectious Diseases, 27(6), 1662. https://doi.org/10.3201/eid2706.204486
  • Mulchandani, R., Wang, Y., Gilbert, M., Van Boeckel, T. P., & Odetokun, I. A. (2023). Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLoS Global Public Health, 3(2), e0001305. https://doi.org/10.1371/journal.pgph.0001305
  • Munita, J. M., & Arias, C. A. (2016). Mechanisms of antibiotic resistance. Virulence Mechanisms of Bacterial Pathogens, 481–511. https://doi.org/10.1128/microbiolspec.vmbf-0016-2015
  • Nelson, J. M., Chiller, T. M., Powers, J. H., & Angulo, F. J. (2007). Fluoroquinolone-resistant Campylobacter species and the withdrawal of fluoroquinolones from use in poultry: A public health success story. Clinical Infectious Diseases, 44(7), 977–980. https://doi.org/10.1086/512369
  • Nguyen, M., Brettin, T., Long, S. W., Musser, J. M., Olsen, R. J., Olson, R., Shukla, M., Stevens, R. L., Xia, F., Yoo, H., & Davis, J. J. (2018). Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Scientific Reports, 8(1), 421. https://doi.org/10.1038/s41598-017-18972-w
  • Nishino, K., Yamasaki, S., Nakashima, R., Zwama, M., & Hayashi-Nishino, M. (2021). Function and inhibitory mechanisms of multidrug efflux pumps. Frontiers in Microbiology, 12, 737288. https://doi.org/10.3389/fmicb.2021.737288
  • Olson, R. D., Assaf, R., Brettin, T., Conrad, N., Cucinell, C., Davis, J. J., Dempsey, D. M., Dickerman, A., Dietrich, E. M., Kenyon, R. W., Kuscuoglu, M., Lefkowitz, E. J., Lu, J., Machi, D., Macken, C., Mao, C., Niewiadomska, A., Nguyen, M., Olsen, G. J., … Stevens, R. L. (2023). Introducing the bacterial and viral bioinformatics resource center (BV-BRC): A resource combining PATRIC, IRD and ViPR. Nucleic Acids Research, 51(D1), D678–D689. https://doi.org/10.1093/nar/gkac1003
  • Papp, M., & Solymosi, N. (2022). Review and comparison of antimicrobial resistance gene databases. Antibiotics, 11(3), 339. https://doi.org/10.3390/antibiotics11030339
  • Peng, Z., Maciel-Guerra, A., Baker, M., Zhang, X., Hu, Y., Wang, W., Rong, J., Zhang, J., Xue, N., Barrow, P., Renney, D., Stekel, D., Williams, P., Liu, L., Chen, J., Li, F., & Dottorini, T. (2022). Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming. PloS Computational Biology, 18(3), e1010018. https://doi.org/10.1371/journal.pcbi.1010018
  • Ramtahal, M. A., Amoako, D. G., Akebe, A. L., Somboro, A. M., Bester, L. A., & Essack, S. Y. (2022). A public health insight into Salmonella in poultry in Africa: A review of the past Decade: 2010-2020. Microbial Drug Resist, 28(6), 710–733. https://doi.org/10.1089/mdr.2021.0384
  • Ren, Y., Chakraborty, T., Doijad, S., Falgenhauer, L., Falgenhauer, J., Goesmann, A., Hauschild, A.-C., Schwengers, O., & Heider, D. (2022). Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics, 38(2), 325–334. https://doi.org/10.1093/bioinformatics/btab681
  • Ren, J., Lee, S. D., Chen, X., Kao, B., Cheng, R., & Cheung, D. (2009, December). Naive Bayes classification of uncertain data. In 2009 Ninth IEEE International Conference on Data Mining (pp. 944–949). IEEE. https://doi.org/10.1109/ICDM.2009.90
  • Reygaert, W. C. (2018). An overview of the antimicrobial resistance mechanisms of bacteria. AIMS Microbiology, 4(3), 482. https://doi.org/10.3934/microbiol.2018.3.482
  • Rodrigues, G. L., Panzenhagen, P., Ferrari, R. R., Dos Santos, A., Paschoalin, V. M. F., & Conte-Junior, C. A. (2020). Frequency of antimicrobial resistance genes in Salmonella from Brazil by in silico whole-genome sequencing analysis: An overview of the last four decades. Frontiers in Microbiology, 11, 1864. https://doi.org/10.3389/fmicb.2020.01864
  • Rokney, A., Valinsky, L., Vranckx, K., Feldman, N., Agmon, V., Moran-Gilad, J., & Weinberger, M. (2020). WGS-based prediction and analysis of antimicrobial resistance in campylobacter jejuni isolates from Israel. Frontiers in Cellular and Infection Microbiology, 10, 365. https://doi.org/10.3389/fcimb.2020.00365
  • Rostron, P., Gaber, S., & Gaber, D. (2016). Raman spectroscopy, review. Laser, 6(1), 50–64.
  • Saikia, D., Jadhav, P., Hole, A. R., Krishna, C. M., & Singh, S. P. (2022). Unraveling the secrets of colistin resistance with label-free raman spectroscopy. Biosensors, 12(9), 749. https://doi.org/10.3390/bios12090749
  • Salam, M. A., Al-Amin, M. Y., Pawar, J. S., Akhter, N., & Lucy, I. B. (2023). Conventional methods and future trends in antimicrobial susceptibility testing. Saudi Journal of Biological Sciences, 30(3), 103582. https://doi.org/10.1016/j.sjbs.2023.103582
  • Samtiya, M., Matthews, K. R., Dhewa, T., & Puniya, A. K. (2022). Antimicrobial resistance in the food chain: Trends, mechanisms, pathways, and possible regulation strategies. Foods, 11(19), 2966. https://doi.org/10.3390/foods11192966
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
  • Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using machine learning and edge cloud computing. Advanced Engineering Informatics, 45, 101101. https://doi.org/10.1016/j.aei.2020.101101
  • Sharaha, U., Suleiman, M., Abu-Aqil, G., Riesenberg, K., Lapidot, I., Salman, A., & Huleihel, M. (2021). Determination of Klebsiella pneumoniae susceptibility to antibiotics using infrared microscopy. Analytical Chemistry, 93(40), 13426–13433. https://doi.org/10.1021/acs.analchem.1c00734
  • Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796
  • Smith, T. C., Male, M. J., Harper, A. L., Kroeger, J. S., Tinkler, G. P., Moritz, E. D., Capuano, A. W., Herwaldt, L. A., & Diekema, D. J. (2009). Methicillin-resistant Staphylococcus aureus (MRSA) strain ST398 is present in midwestern U.S. Swine and swine workers. Public Library of Science One, 4(1), e4258. https://doi.org/10.1371/journal.pone.0004258
  • Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130. https://doi.org/10.11919/j.issn.1002-0829.215044
  • Spänig, S., & Heider, D. (2019). Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Mining, 12(1), 1–29. https://doi.org/10.1186/s13040-019-0196-x
  • Stein, L. (2001). Genome annotation: From sequence to biology. Nature Reviews Genetics, 2(7), 493–503. https://doi.org/10.1038/35080529
  • Stratev, D., & Odeyemi, O. A. (2016). Antimicrobial resistance of Aeromonas hydrophila isolated from different food sources: A mini-review. Journal of Infection and Public Health, 9(5), 535–544. https://doi.org/10.1016/j.jiph.2015.10.006
  • Suleiman, M., Abu-Aqil, G., Sharaha, U., Riesenberg, K., Lapidot, I., Salman, A., & Huleihel, M. (2022). Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa’s susceptibility to antibiotics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 274, 121080. https://doi.org/10.1016/j.saa.2022.121080
  • Sun, S., Selmer, M., Andersson, D. I., & Cloeckaert, A. (2014). Resistance to β-lactam antibiotics conferred by point mutations in penicillin-binding proteins PBP3, PBP4 and PBP6 in Salmonella enterica. Public Library of Science ONE, 9(5), e97202. https://doi.org/10.1371/journal.pone.0097202
  • Sunuwar, J., & Azad, R. K. (2021). A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Briefings in Bioinformatics, 22(6), bbab179. https://doi.org/10.1093/bib/bbab179
  • Tang, X., Shang, J., Ji, Y., & Sun, Y. (2023). Plasme: A tool to identify PLASMid contigs from short-read assemblies using transformer. Nucleic Acids Research, 51(15), e83–e83. https://doi.org/10.1093/nar/gkad578
  • Turner, N. A., Sharma-Kuinkel, B. K., Maskarinec, S. A., Eichenberger, E. M., Shah, P. P., Holland, T. L., & Fowler Jr, V. G. (2019). Methicillin-resistant Staphylococcus aureus: An overview of basic and clinical research. Nature Reviews Microbiology, 17(4), 203–218. https://doi.org/10.1038/s41579-018-0147-4
  • Uddin, T. M., Chakraborty, A. J., Khusro, A., Zidan, B. R. M., Mitra, S., Emran, T. B., Dhama, K., Ripon, M. K. H., Gajdács, M., Sahibzada, M. U. K., Hossain, M. J., & Koirala, N. (2021). Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects. Journal of Infection and Public Health, 14(12), 1750–1766. https://doi.org/10.1016/j.jiph.2021.10.020
  • Uelze, L., Grützke, J., Borowiak, M., Hammerl, J. A., Juraschek, K., Deneke, C., Tausch, S. H., & Malorny, B. (2020). Typing methods based on whole genome sequencing data. One Health Outlook, 2(1), 1–19. https://doi.org/10.1186/s42522-020-0010-1
  • UN. (2020). Antimicrobial resistance: a global threat. UNEP - UN Environment Programme. http://www.unep.org/explore-topics/chemicals-waste/what-we-do/emerging-issues/antimicrobial-resistance-global-threat
  • Urban-Chmiel, R., Marek, A., Stępień-Pyśniak, D., Wieczorek, K., Dec, M., Nowaczek, A., & Osek, J. (2022). Antibiotic resistance in bacteria-A review. Antibiotics, 11(8), 1079. https://doi.org/10.3390/antibiotics11081079
  • Van Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., Teillant, A., & Laxminarayan, R. (2015). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences, 112(18), 5649–5654. https://doi.org/10.1073/pnas.1503141112
  • Van Boeckel, T. P., Glennon, E. E., Chen, D., Gilbert, M., Robinson, T. P., Grenfell, B. T., Levin, S. A., Bonhoeffer, S., & Laxminarayan, R. (2017). Reducing antimicrobial use in food animals. Science, 357(6358), 1350–1352. https://doi.org/10.1126/science.aao1495
  • Veltcheva, D., Colles, F. M., Varga, M., Maiden, M. C., & Bonsall, M. B. (2022). Emerging patterns of fluoroquinolone resistance in Campylobacter jejuni in the UK [1998–2018]. Microbial Genomics, 8(9). https://doi.org/10.1099/mgen.0.000875
  • Vinayamohan, P. G., Pellissery, A. J., & Venkitanarayanan, K. (2022). Role of horizontal gene transfer in the dissemination of antimicrobial resistance in food animal production. Current Opinion in Food Science, 47, 100882. https://doi.org/10.1016/j.cofs.2022.100882
  • Wang, W., Baker, M., Hu, Y., Xu, J., Yang, D., Maciel-Guerra, A., Xue, N., Li, H., Yan, S., Li, M., Bai, Y., Dong, Y., Peng, Z., Ma, J., Li, F., & Dottorini, T. (2021). Whole-genome sequencing and machine learning analysis of Staphylococcus aureus from multiple heterogeneous sources in China reveals common genetic traits of antimicrobial resistance. mSystems [Internet], 6(3), e01185–20. https://doi.org/10.1128/msystems.01185-20
  • Wang, X. L., Li, L., Li, S. M., Huang, J. Y., Fan, Y. P., Yao, Z. J., Ye, X. H., & Chen, S. D. (2017). Phenotypic and molecular characteristics of Staphylococcus aureus and methicillin-resistant Staphylococcus aureus in slaughterhouse pig-related workers and control workers in Guangdong Province, China. Epidemiology & Infection, 145(9), 1843–1851. https://doi.org/10.1017/S0950268817000085
  • WHO. (2017a). Stop using antibiotics in healthy animals to preserve their effectiveness. https://www.who.int/news/item/07-11-2017-stop-using-antibiotics-in-healthy-animals-to-prevent-the-spread-of-antibiotic-resistance
  • WHO. (2017b). WHO publishes list of bacteria for which new antibiotics are urgently needed. https://www.who.int/news/item/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed
  • WHO. (2019a). New report calls for urgent action to avert antimicrobial resistance crisis. https://www.who.int/news/item/29-04-2019-new-report-calls-for-urgent-action-to-avert-antimicrobial-resistance-crisis
  • WHO. (2019b). UN interagency coordination group on antimicrobial resistance presents its report to the UN SG. https://www.who.int/news/item/28-04-2019-un-interagency-coordination-group-on-antimicrobial-resistance-presents-its-report-to-the-un-sg
  • Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017). Completing bacterial genome assemblies with multiplex MinION sequencing. Microbial Genomics, 3(10). https://doi.org/10.1099/mgen.0.000132
  • Wick, R. R., Judd, L. M., Gorrie, C. L., Holt, K. E., & Phillippy, A. M. (2017). Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Computational Biology, 13(6), e1005595. https://doi.org/10.1371/journal.pcbi.1005595
  • Wieczorek, K., & Osek, J. (2013). Antimicrobial resistance mechanisms among Campylobacter. BioMed Research International, 2013, 1–12. https://doi.org/10.1155/2013/340605
  • Xie, R., Li, J., Wang, J., Dai, W., Leier, A., Marquez-Lago, T. T., Akutsu, T., Lithgow, T., Song, J., & Zhang, Y. (2021). DeepVF: A deep learning-based hybrid framework for identifying virulence factors using the stacking strategy. Briefings in Bioinformatics, 22(3), bbaa125. https://doi.org/10.1093/bib/bbaa125
  • Xiong, Z., Cui, Y., Liu, Z., Zhao, Y., Hu, M., & Hu, J. (2020). Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Computational Materials Science, 171, 109203. https://doi.org/10.1016/j.commatsci.2019.109203
  • Yadav, S., & Kapley, A. (2021). Antibiotic resistance: Global health crisis and metagenomics. Biotechnology Reports, 29, e00604. https://doi.org/10.1016/j.btre.2021.e00604
  • Yadav, S., & Shukla, S. (2016, February). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE. https://doi.org/10.1109/IACC.2016.25
  • Yang, A., Zhang, W., Wang, J., Yang, K., Han, Y., & Zhang, L. (2020). Review on the application of machine learning algorithms in the sequence data mining of DNA. Frontiers in Bioengineering and Biotechnology, 8, 1032. https://doi.org/10.3389/fbioe.2020.01032
  • Yasir, M., Karim, A. M., Malik, S. K., Bajaffer, A. A., & Azhar, E. I. (2022). Application of decision-tree-based machine learning algorithms for prediction of antimicrobial resistance. Antibiotics, 11(11), 1593. https://doi.org/10.3390/antibiotics11111593
  • Yuan, J., Tang, F., Qi, Z., & Zhao, H. (2023). Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm. Food Quality and Safety, 7, 7. https://doi.org/10.1093/fqsafe/fyac071
  • Zhang, Z., & Castelló, A. (2017). Principal components analysis in clinical studies. Annals of Translational Medicine, 5(17), 351–351. https://doi.org/10.21037/atm.2017.07.12
  • Zhang, Q., Shen, Z., & Huang, D. S. (2019). Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network. Scientific Reports, 9(1), 8484. https://doi.org/10.1038/s41598-019-44966-x
  • Zhu, Q., Gao, S., Xiao, B., He, Z., Hu, S., & Yin, Y. (2023). Plasmer: An accurate and sensitive bacterial plasmid prediction tool based on machine learning of shared k-mers and genomic features. Microbiology Spectrum, 11(3), e04645–22. https://doi.org/10.1128/spectrum.04645-22