MACHINE LEARNING-BASED PREDICTION OF ANTIBIOTIC RESISTANCE USING PHENOTYPIC SUSCEPTIBILITY PATTERNS AND RESISTANCE GENE PROFILES
Keywords:
Antimicrobial resistance, Machine learning, Phenotypic susceptibility, Resistance genes, Antibiotic resistance prediction
Abstract
Antibiotic resistance is one of the current challenges for successful antimicrobial therapy and calls for analytical methods that can combine phenotypic and molecular data. The aim of this study was to create machine-learning models that predict antibiotic-specific resistance that can be derived from phenotypic susceptibility patterns, resistance gene profiles, and contextual variables. Susceptibility results for amoxicillin, ciprofloxacin, meropenem, vancomycin, and colistin were analyzed besides KPC, NDM-1, OXA-48, and VIM resistance genes. Data preprocessing comprised categorical encoding, evaluation of missing data, partitioning of data in groups by patient identifier, and excluding each target antibiotic from its respective predictor set to avoid information leakage. Accuracy, precision, sensitivity, specificity, F1-score, ROC-AUC, and PR-AUC were used to compare Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and Gradient Boosting models. Susceptibility categories and resistance gene frequencies were fairly even, and genotype-phenotype relationships were not strong. The best mean ROC-AUCs were not far off from chance-level discrimination by the machine-learning algorithms. The inclusion of resistance gene and contextual variables did not significantly increase the predictive value over phenotypic variables. These results will suggest that a more accurate prediction of resistance will need to be developed with more genomic data, more populations, and validation outside of the clinical setting before it can be used in the clinic. However, the framework offers a clear foundation to assess integrated antimicrobial resistance predictors
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References
1.Alcock, B. P., Huynh, W., Chalil, R., Smith, K. W., Raphenya, A. R., Wlodarski, M. A., ... & McArthur, A. G. (2023). CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic acids research, 51(D1), D690-D699.
2.Anahtar, M. N., Yang, J. H., & Kanjilal, S. (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.
3.Ardila, C. M., Yadalam, P. K., & González-Arroyave, D. (2024). Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests. PeerJ, 12, e18213.
4.Bano, N., Mohammed, S. A., & Raza, K. (2025). Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review. Journal of Drug Targeting, 33(3), 384-396.
5.Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente-Nunez, C. (2025). Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance. npj Antimicrobials and Resistance, 3(1), 2.
6.Chindelevitch, L., van Dongen, M., Graz, H., Pedrotta, A., Suresh, A., Uplekar, S., ... & Wheeler, N. (2023). Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance. PLoS Computational Biology, 19(6), e1011129.
7.de la Lastra, J. M. P., Wardell, S. J., Pal, T., de la Fuente-Nunez, C., & Pletzer, D. (2024). From data to decisions: leveraging artificial intelligence and machine learning in combating antimicrobial resistance–a comprehensive review. Journal of medical systems, 48(1), 71.
8.Farhat, N., & Khan, A. U. (2020). Evolving trends of New Delhi Metallo-betalactamse (NDM) variants: A threat to antimicrobial resistance. Infection, Genetics and Evolution, 86, 104588.
9.Fernandez, G. (2022). Turning the juggernaut. Lancet Planetary Health, 6(2), E75-E75.
10.Fistarol, B. F., Gervasio, J. D., & Szöllősi, G. J. (2025). Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus. npj Antimicrobials and Resistance, 3(1), 100.
11.Gao, Y., Li, H., Zhao, C., Li, S., Yin, G., & Wang, H. (2024). Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data. Frontiers in microbiology, 14, 1320312.
12.Humphries, R. M., Bragin, E., Parkhill, J., Morales, G., Schmitz, J. E., & Rhodes, P. A. (2023). Machine-learning model for prediction of cefepime susceptibility in Escherichia coli from whole-genome sequencing data. Journal of clinical microbiology, 61(3), e01431-22.
13.Kim, J. I., Maguire, F., Tsang, K. K., Gouliouris, T., Peacock, S. J., McAllister, T. A., ... & Beiko, R. G. (2022). Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective. Clinical microbiology reviews, 35(3), e00179-21.
14.Li, X., Zhang, Z., Liang, B., Ye, F., & Gong, W. (2021). A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria. The Journal of Antibiotics, 74(12), 838-849.
15.Liu, Z., Deng, D., Lu, H., Sun, J., Lv, L., Li, S., ... & Wang, G. (2020). Evaluation of machine learning models for predicting antimicrobial resistance of Actinobacillus pleuropneumoniae from whole genome sequences. Frontiers in microbiology, 11, 48.
16.Lv, G., & Wang, Y. (2024). Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technology and Health Care, 32(5), 2865-2882.
17.Moat, J., Zovoilis, A., Steinkey, R., Zaheer, R., McAllister, T., & Laing, C. (2025). Machine learning methods to identify markers and predict antimicrobial resistance in Escherichia coli. Canadian Journal of Microbiology, 71, 1-15.
18.Noman, S. M., Zeeshan, M., Arshad, J., Deressa Amentie, M., Shafiq, M., Yuan, Y., ... & Jiao, X. (2023). Machine learning techniques for antimicrobial resistance prediction of Pseudomonas aeruginosa from whole genome sequence data. Computational intelligence and neuroscience, 2023(1), 5236168.
19.Orcales, F., Moctezuma Tan, L., Johnson-Hagler, M., Suntay, J. M., Ali, J., Recto, K., ... & Pennings, P. (2024). Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper. PLoS computational biology, 20(12), e1012579.
20.Petrin, S., Orsini, M., Massaro, A., Olsen, J. E., Barco, L., & Losasso, C. (2023). Phenotypic and genotypic antimicrobial resistance correlation and plasmid characterization in Salmonella spp. isolates from Italy reveal high heterogeneity among serovars. Frontiers in public health, 11, 1221351.
21.Ren, Y., Chakraborty, T., Doijad, S., Falgenhauer, L., Falgenhauer, J., Goesmann, A., ... & Heider, D. (2022). Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics, 38(2), 325-334.
22.Siam, M. S., & Faiza, A. T. (2025). Antibiotic resistance tracking dataset (Version 2) [Data set]. Mendeley Data. https://doi.org/10.17632/h4byb28gcv.2
23.Singh, R., Lin, Y. W., Zhao, J., & Li, J. (2026). Artificial intelligence and machine learning in antimicrobial discovery, resistance prediction, and precision therapy. International Journal of Antimicrobial Agents, 107715.
24.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.
25.Talamantes-Becerra, B., Wickramarachchi, A., Bauer, D. C., Sng, L., Nguyen, A., Twine, N., & Leroux, H. (2024, September). Prediction of antibiotic susceptibility in E. coli Isolates using machine learning. In Health. Innovation. Community: It Starts With Us: Papers from the 28th Australian Digital Health and Health Informatics Conference (HIC 2024)–Brisbane, Australia, 5–7 August 2024 (pp. 150-155). 1 Oliver's Yard, 55 City Road, London, EC1Y 1SP: SAGE Publications Pvt. Ltd.
26.Valavarasu, S., Sangu, Y., & Mahapatra, T. (2025). Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning. Scientific reports, 15(1), 30509.
27.Van Camp, P. J., Haslam, D. B., & Porollo, A. (2020). Prediction of antimicrobial resistance in gram-negative bacteria from whole-genome sequencing data. Frontiers in microbiology, 11, 1013.
28.Wan, F., Tong, W., Wu, W., Liu, J., Huang, H., Zhang, P., & Jiang, W. (2026). Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance in Escherichia coli. Frontiers in Microbiology, 17, 1842717.
2.Anahtar, M. N., Yang, J. H., & Kanjilal, S. (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.
3.Ardila, C. M., Yadalam, P. K., & González-Arroyave, D. (2024). Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests. PeerJ, 12, e18213.
4.Bano, N., Mohammed, S. A., & Raza, K. (2025). Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review. Journal of Drug Targeting, 33(3), 384-396.
5.Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente-Nunez, C. (2025). Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance. npj Antimicrobials and Resistance, 3(1), 2.
6.Chindelevitch, L., van Dongen, M., Graz, H., Pedrotta, A., Suresh, A., Uplekar, S., ... & Wheeler, N. (2023). Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance. PLoS Computational Biology, 19(6), e1011129.
7.de la Lastra, J. M. P., Wardell, S. J., Pal, T., de la Fuente-Nunez, C., & Pletzer, D. (2024). From data to decisions: leveraging artificial intelligence and machine learning in combating antimicrobial resistance–a comprehensive review. Journal of medical systems, 48(1), 71.
8.Farhat, N., & Khan, A. U. (2020). Evolving trends of New Delhi Metallo-betalactamse (NDM) variants: A threat to antimicrobial resistance. Infection, Genetics and Evolution, 86, 104588.
9.Fernandez, G. (2022). Turning the juggernaut. Lancet Planetary Health, 6(2), E75-E75.
10.Fistarol, B. F., Gervasio, J. D., & Szöllősi, G. J. (2025). Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus. npj Antimicrobials and Resistance, 3(1), 100.
11.Gao, Y., Li, H., Zhao, C., Li, S., Yin, G., & Wang, H. (2024). Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data. Frontiers in microbiology, 14, 1320312.
12.Humphries, R. M., Bragin, E., Parkhill, J., Morales, G., Schmitz, J. E., & Rhodes, P. A. (2023). Machine-learning model for prediction of cefepime susceptibility in Escherichia coli from whole-genome sequencing data. Journal of clinical microbiology, 61(3), e01431-22.
13.Kim, J. I., Maguire, F., Tsang, K. K., Gouliouris, T., Peacock, S. J., McAllister, T. A., ... & Beiko, R. G. (2022). Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective. Clinical microbiology reviews, 35(3), e00179-21.
14.Li, X., Zhang, Z., Liang, B., Ye, F., & Gong, W. (2021). A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria. The Journal of Antibiotics, 74(12), 838-849.
15.Liu, Z., Deng, D., Lu, H., Sun, J., Lv, L., Li, S., ... & Wang, G. (2020). Evaluation of machine learning models for predicting antimicrobial resistance of Actinobacillus pleuropneumoniae from whole genome sequences. Frontiers in microbiology, 11, 48.
16.Lv, G., & Wang, Y. (2024). Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technology and Health Care, 32(5), 2865-2882.
17.Moat, J., Zovoilis, A., Steinkey, R., Zaheer, R., McAllister, T., & Laing, C. (2025). Machine learning methods to identify markers and predict antimicrobial resistance in Escherichia coli. Canadian Journal of Microbiology, 71, 1-15.
18.Noman, S. M., Zeeshan, M., Arshad, J., Deressa Amentie, M., Shafiq, M., Yuan, Y., ... & Jiao, X. (2023). Machine learning techniques for antimicrobial resistance prediction of Pseudomonas aeruginosa from whole genome sequence data. Computational intelligence and neuroscience, 2023(1), 5236168.
19.Orcales, F., Moctezuma Tan, L., Johnson-Hagler, M., Suntay, J. M., Ali, J., Recto, K., ... & Pennings, P. (2024). Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper. PLoS computational biology, 20(12), e1012579.
20.Petrin, S., Orsini, M., Massaro, A., Olsen, J. E., Barco, L., & Losasso, C. (2023). Phenotypic and genotypic antimicrobial resistance correlation and plasmid characterization in Salmonella spp. isolates from Italy reveal high heterogeneity among serovars. Frontiers in public health, 11, 1221351.
21.Ren, Y., Chakraborty, T., Doijad, S., Falgenhauer, L., Falgenhauer, J., Goesmann, A., ... & Heider, D. (2022). Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics, 38(2), 325-334.
22.Siam, M. S., & Faiza, A. T. (2025). Antibiotic resistance tracking dataset (Version 2) [Data set]. Mendeley Data. https://doi.org/10.17632/h4byb28gcv.2
23.Singh, R., Lin, Y. W., Zhao, J., & Li, J. (2026). Artificial intelligence and machine learning in antimicrobial discovery, resistance prediction, and precision therapy. International Journal of Antimicrobial Agents, 107715.
24.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.
25.Talamantes-Becerra, B., Wickramarachchi, A., Bauer, D. C., Sng, L., Nguyen, A., Twine, N., & Leroux, H. (2024, September). Prediction of antibiotic susceptibility in E. coli Isolates using machine learning. In Health. Innovation. Community: It Starts With Us: Papers from the 28th Australian Digital Health and Health Informatics Conference (HIC 2024)–Brisbane, Australia, 5–7 August 2024 (pp. 150-155). 1 Oliver's Yard, 55 City Road, London, EC1Y 1SP: SAGE Publications Pvt. Ltd.
26.Valavarasu, S., Sangu, Y., & Mahapatra, T. (2025). Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning. Scientific reports, 15(1), 30509.
27.Van Camp, P. J., Haslam, D. B., & Porollo, A. (2020). Prediction of antimicrobial resistance in gram-negative bacteria from whole-genome sequencing data. Frontiers in microbiology, 11, 1013.
28.Wan, F., Tong, W., Wu, W., Liu, J., Huang, H., Zhang, P., & Jiang, W. (2026). Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance in Escherichia coli. Frontiers in Microbiology, 17, 1842717.
Published
2026-05-27
How to Cite
Chinedu E. Okafor, Amina Yusuf, & Ibrahim M. Bello. (2026). MACHINE LEARNING-BASED PREDICTION OF ANTIBIOTIC RESISTANCE USING PHENOTYPIC SUSCEPTIBILITY PATTERNS AND RESISTANCE GENE PROFILES. IJRDO - JOURNAL OF BIOLOGICAL SCIENCE, 12(2), 121-137. https://doi.org/10.69980/bs.v12i2.6807
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Articles
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