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Journals / Fenerbahçe Üniversitesi Sağlık Bilimleri Dergisi / 2026 / Cilt: 5 Sayı: 2

The Comparison of Machine Learning Algorithms for Microbiome Data

Mikrobiyom Verileri için Makine Öğrenme Algoritmalarının Karşılaştırılması

Özlem Akay(GAZİANTEP İSLAM BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ)0000-0002-9539-7252
Gülfer Yakici(GAZİANTEP İSLAM BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ)0000-0001-6486-3209
Pages
206–224
Publication Date
2025-08-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show that the random forest (RF) algorithm exhibits the highest classification performance, successfully distinguishing between cultured and uncultured blood microbiota.

📌 3 Key Takeaways

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Objective & Hypothesis:The application of next-generation sequencing (NGS) technologies has enabled the identification of both culturable and non-culturable microorganisms in blood samples, revealing their potential roles in systemic infections and immune responses.
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Key Findings & Data:However, the complexity and high dimensionality of microbiome data present significant challenges for analysis.
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Conclusion & Impact:Results show that the random forest (RF) algorithm exhibits the highest classification performance, successfully distinguishing between cultured and uncultured blood microbiota.
🔬Design:Modeling & Simulation
📍Scope:56 samples
⚙️Variables:Makine öğrenmesi · Mikrobiom · Kan Mikrobiyatası

Abstract

The application of next-generation sequencing (NGS) technologies has enabled the identification of both culturable and non-culturable microorganisms in blood samples, revealing their potential roles in systemic infections and immune responses. However, the complexity and high dimensionality of microbiome data present significant challenges for analysis. In this study, it was evaluated the performance of various machine learning (ML) algorithms, including logistic regression, random forest (RF), decision tree, and support vector machines (SVM), in classifying 16S rRNA gene sequencing data of blood microbiota into cultured and uncultured groups. The dataset used in this study, obtained from Kalfin and Panaiotov, consists of 16S rRNA gene sequences from a total of 18,093 OTUs and 62 observations, including control samples. After excluding the six control samples, 56 samples from target sequencing of cultured and non-cultured blood samples of healthy individuals were analyzed. Results show that the random forest (RF) algorithm exhibits the highest classification performance, successfully distinguishing between cultured and uncultured blood microbiota. In the study, the potential of ML techniques in microbiome research was evaluated and the effectiveness and accuracy of these techniques in the analysis of microbiome data were investigated.

Özet

Yeni nesil dizileme (NGS) teknolojilerinin uygulanması, kan örneklerinde hem kültürlenebilen hem de kültürlenemeyen mikroorganizmaların tanımlanmasını sağlayarak, sistemik enfeksiyonlarda ve bağışıklık tepkilerinde potansiyel rollerini ortaya koymuştur. Ancak, mikrobiyom verilerinin karmaşıklığı ve yüksek boyutluluğu, analiz için önemli zorluklar sunmaktadır. Bu çalışmada, lojistik regresyon, rastgele orman (RF), karar ağacı ve destek vektör makineleri (SVM) dahil olmak üzere çeşitli makine öğrenimi (ML) algoritmalarının, kan mikrobiyotasının 16S rRNA gen dizileme verilerini kültürlenmiş ve kültürlenmemiş gruplara sınıflandırmadaki performansı değerlendirilmiştir. Çalışmada kullanılan veri seti, Kalfin ve Panaiotov’dan elde edilen 16S rRNA gen dizileri ile oluşturulmuş olup, toplamda 18.093 OTU ve 62 gözlem içermektedir; bunlar arasında kontrol örnekleri de bulunmaktadır. Altı kontrol örneği çalışmadan çıkarıldıktan sonra, sağlıklı bireylerden alınan kültürlü ve kültürsüz kan örneklerine ait 56 örnek üzerinde analizler yapılmıştır. Bulgular, rastgele orman (RF) algoritmasının en yüksek sınıflandırma performansını sergilediğini ve kültürlenmiş ve kültürlenmemiş kan mikrobiyotası arasında başarılı bir şekilde ayrım yaptığını göstermiştir. Çalışmada, mikrobiyom araştırmalarında ML tekniklerinin potansiyeli değerlendirilmiş ve bu tekniklerin mikrobiyom verilerinin analizindeki etkinliği ve doğruluğu, araştırılmıştır.

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Cite This Article

Akay, Ö., Yakici, G. (2025). The Comparison of Machine Learning Algorithms for Microbiome Data. Fenerbahçe Üniversitesi Sağlık Bilimleri Dergisi, Cilt: 5 Sayı: 2, 206–224. https://doi.org/10.56061/fbujohs.1636654

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References (67)

  1. Abdulqader, Q. M. (2017). Applying the Binary Logistic Regression Analysis on The Medical Data. Science Journal of University of Zakho, 5, 330-334. https://doi.org/10.25271/2017.5.4.388
  2. Aggarwal, N., Kitano, S., Puah, G. R. Y., Kittelmann, S., Hwang, I. Y., & Chang, M. W. (2023). Microbiome and Human Health: Current Understanding, Engineering, and Enabling Technologies. Chem Rev, 123(1), 31-72. https://doi.org/10.1021/acs.chemrev.2c00431
  3. Alam, M. S., & Vuong, S. T. (2013). Random forest classification for detecting android malware. 2013 IEEE international conference on green computing and communications and IEEE Internet of Things and IEEE cyber, physical and social computing, Beijing, China, 2013, pp. 663-669. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.122
  4. Alon, T. (2023). Ultimate Guide to PR-AUC: Calculations, uses, and limitations. Retrieved 05.06.2024 from https://www.aporia.com/learn/ultimate-guide-to-precision-recall-auc-understanding-calculating-using-pr-auc-in-ml/
  5. AUC, C. (2024). Retrieved 05.06.2024 from https://cran.r-project.org/web/packages/mikropml/vignettes/introduction.html
  6. Bangdiwala, S. I. (2018). Regression: binary logistic. Int J Inj Contr Saf Promot, 25(3), 336-338. https://doi.org/10.1080/17457300.2018.1486503
  7. Beck, D., & Foster, J. A. (2015). Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis. BioData mining, 8, 1-9. https://doi.org/10.1186/s13040-015-0055-3
  8. Begg, R. K., Palaniswami, M., & Owen, B. (2005). Support vector machines for automated gait classification. IEEE transactions on Biomedical Engineering, 52(5), 828-838. https://doi.org/10.1109/TBME.2005.845241
  9. Bhavsar, H., & Panchal, M. H. (2012). A review on support vector machine for data classification. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), 185-189.
  10. Bilgin, B., & Hanci, H. (2023). Gut Microbiota and Its Importance for Our Health. Pharmata, 3(3), 71-73. https://doi.org/10.5152/Pharmata.2023.1318972
  11. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  12. Buckley, S. J., & Harvey, R. J. (2021). Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus pyogenes [Mini Review]. Frontiers in Cellular and Infection Microbiology, 11. https://doi.org/10.3389/fcimb.2021.809560
  13. Cheng, H. S., Tan, S. P., Wong, D. M. K., Koo, W. L. Y., Wong, S. H., & Tan, N. S. (2023). The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges. Int J Mol Sci, 24(6). https://doi.org/10.3390/ijms24065633
  14. Chidambaram, S., & Srinivasagan, K. (2019). Performance evaluation of support vector machine classification approaches in data mining. Cluster Computing, 22, 189-196. https://doi.org/10.1007/s10586-018-2036-z
  15. Ciftciler, R., & Ciftciler, A. E. (2022). The importance of microbiota in hematology. Transfus Apher Sci, 61(2), 103320. https://doi.org/10.1016/j.transci.2021.103320
  16. Costa, S. P., & Carvalho, C. M. (2022). Burden of bacterial bloodstream infections and recent advances for diagnosis. Pathog Dis, 80(1). https://doi.org/10.1093/femspd/ftac027
  17. D'Elia, D., Truu, J., Lahti, L., Berland, M., Papoutsoglou, G., Ceci, M., Zomer, A., Lopes, M. B., Ibrahimi, E., Gruca, A., Nechyporenko, A., Frohme, M., Klammsteiner, T., Pau, E. C. S., Marcos-Zambrano, L. J., Hron, K., Pio, G., Simeon, A., Suharoschi, R.,. Claesson, M. J. (2023). Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front Microbiol, 14, 1257002. https://doi.org/10.3389/fmicb.2023.1257002
  18. Dembla, G. (2020). Intuition behind Log-loss score. Towards Data Science.
  19. Demirci, M., Saribas, A. S., Siadat, S. D., & Kocazeybek, B. S. (2023). Editorial: Blood microbiota in health and disease. Front Cell Infect Microbiol, 13, 1187247. https://doi.org/10.3389/fcimb.2023.1187247
  20. Emery, D. C., Cerajewska, T. L., Seong, J., Davies, M., Paterson, A., Allen-Birt, S. J., & West, N. X. (2020). Comparison of Blood Bacterial Communities in Periodontal Health and Periodontal Disease. Front Cell Infect Microbiol, 10, 577485. https://doi.org/10.3389/fcimb.2020.577485
  21. Freitas, P., Silva, F., Sousa, J. V., Ferreira, R. M., Figueiredo, C., Pereira, T., & Oliveira, H. P. (2023). Machine learning-based approaches for cancer prediction using microbiome data. Scientific reports, 13(1), 11821. https://doi.org/10.1038/s41598-023-38670-0
  22. Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. Int. J. Intell. Technol. Appl. Stat, 11(2), 105-111. https://doi.org/10.6148/IJITAS.201806_11(2).0003
  23. Github-1. (2024). Retrieved 06.12.2024 from https://github.com/yhodzhev/blood_microbiota
  24. Goraya, M. U., Li, R., Mannan, A., Gu, L., Deng, H., & Wang, G. (2022). Human circulating bacteria and dysbiosis in non-infectious diseases. Front Cell Infect Microbiol, 12, 932702. https://doi.org/10.3389/fcimb.2022.932702
  25. Gotschlich, E. C., Colbert, R. A., & Gill, T. (2019). Methods in microbiome research: Past, present, and future. Best Pract Res Clin Rheumatol, 33(6), 101498. https://doi.org/10.1016/j.berh.2020.101498
  26. Jijo, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2, 20-28. https://doi.org/10.38094/jastt20165
  27. Kajihara, M., Koido, S., Kanai, T., Ito, Z., Matsumoto, Y., Takakura, K., Saruta, M., Kato, K., Odamaki, T., Xiao, J. Z., Sato, N., & Ohkusa, T. (2019). Characterisation of blood microbiota in patients with liver cirrhosis. Eur J Gastroenterol Hepatol, 31(12), 1577-1583. https://doi.org/10.1097/MEG.0000000000001494
  28. Khan, I., Khan, I., Jianye, Z., Xiaohua, Z., Khan, M., Hilal, M. G., Kakakhel, M. A., Mehmood, A., Lizhe, A., & Zhiqiang, L. (2022). Exploring blood microbial communities and their influence on human cardiovascular disease. J Clin Lab Anal, 36(4), e24354. https://doi.org/10.1002/jcla.24354
  29. Khan, I., Khan, I., Usman, M., Xiao Wei, Z., Ping, X., Khan, S., Khan, F., Jianye, Z., Zhiqiang, L., & Lizhe, A. (2022). Circulating microbiota and metabolites: Insights into cardiovascular diseases. J Clin Lab Anal, 36(12), e24779. https://doi.org/10.1002/jcla.24779
  30. Kim, H., Na, J. E., Kim, S., Kim, T. O., Park, S. K., Lee, C. W., Kim, K. O., Seo, G. S., Kim, M. S., Cha, J. M., Koo, J. S., & Park, D. I. (2023). A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis. Microorganisms, 12(1). https://doi.org/10.3390/microorganisms12010036
  31. Kolena, T. w. (2024). Retrieved 06.12.2024 from https://docs.kolena.com/metrics/cohens-kappa/
  32. Kouchaki, S., Yang, Y., Lachapelle, A., Walker, T. M., Walker, A. S.,, C. C., Peto, T. E. A., Crook, D. W., & Clifton, D. A. (2020). Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking [Original Research]. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.00667
  33. Huilgol, P. (2025). Retrieved 18.04.2025 from https://www.analyticsvidhya.com/articles/precision-and-recall-in-machine-learning/
  34. Lee, E. J., Sung, J., Kim, H. L., & Kim, H. N. (2022). Whole-Genome Sequencing Reveals Age-Specific Changes in the Human Blood Microbiota. J Pers Med, 12(6). https://doi.org/10.3390/jpm12060939
  35. Li, L., Mendis, N., Trigui, H., Oliver, J. D., & Faucher, S. P. (2014). The importance of the viable but non-culturable state in human bacterial pathogens. Front Microbiol, 5, 258. https://doi.org/10.3389/fmicb.2014.00258
  36. Lin, Y., Lee, Y., & Wahba, G. (2002). Support vector machines for classification in nonstandard situations. Machine Learning, 46, 191-202. https://doi.org/10.1023/A:1012406528296
  37. Liu, Y. X., Qin, Y., Chen, T., Lu, M., Qian, X., Guo, X., & Bai, Y. (2021). A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell, 12(5), 315-330. https://doi.org/10.1007/s13238-020-00724-8
  38. Log, G. (2024). Retrieved 05.06.2024 from https://rdrr.io/github/jeffreyevans/rfUtilities/man/logLoss.html
  39. Loganathan, T., & Priya Doss, C. G. (2022). The influence of machine learning technologies in gut microbiome research and cancer studies- A review. Life Sci, 311(Pt A), 121118. https://doi.org/10.1016/j.lfs.2022.121118
  40. Mair, R. D., & Sirich, T. L. (2019). Blood Microbiome in CKD: Should We Care? Clin J Am Soc Nephrol, 14(5), 648-649. https://doi.org/10.2215/CJN.03420319
  41. Molina-Menor, E., Gimeno-Valero, H., Pascual, J., Pereto, J., & Porcar, M. (2020). High Culturable Bacterial Diversity From a European Desert: The Tabernas Desert. Front Microbiol, 11, 583120. https://doi.org/10.3389/fmicb.2020.583120
  42. Nannapaneni, P., Hertwig, F., Depke, M., Hecker, M., Mäder, U., Völker, U., Steil, L., & van Hijum, S. (2012). Defining the structure of the general stress regulon of Bacillus subtilis using targeted microarray analysis and random forest classification. Microbiology (Reading), 158(Pt 3), 696-707. https://doi.org/10.1099/mic.0.055434-0
  43. Olugbenga, M. (2024). Retrieved 06.12.2024 from https://neptune.ai/blog/balanced-accuracy
  44. Othman, M. F. B., Abdullah, N. B., & Kamal, N. F. B. (2011). MRI brain classification using support vector machine. 2011 fourth international conference on modeling, simulation and applied optimization, Kuala Lumpur, Malaysia, 2011, pp. 1-4. https://doi.org/10.1109/ICMSAO.2011.5775605
  45. Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222. https://doi.org/10.1080/01431160412331269698
  46. Panaiotov, S., Hodzhev, Y., Tsafarova, B., Tolchkov, V., & Kalfin, R. (2021). Culturable and Non-Culturable Blood Microbiota of Healthy Individuals. Microorganisms, 9(7). https://doi.org/10.3390/microorganisms9071464
  47. Porras, A. M., & Brito, I. L. (2019). The internationalization of human microbiome research. Curr Opin Microbiol, 50, 50-55. https://doi.org/10.1016/j.mib.2019.09.012
  48. Potgieter, M., Bester, J., Kell, D. B., & Pretorius, E. (2015). The dormant blood microbiome in chronic, inflammatory diseases. FEMS Microbiology Reviews, 39(4), 567-591. https://doi.org/10.1093/femsre/fuv013
  49. Rai, K., Devi, M. S., & Guleria, A. (2016). Decision tree based algorithm for intrusion detection. International Journal of Advanced Networking and Applications, 7(4), 2828.
  50. Rana, S., Midi, H. B., & Sarkar, S. K. (2010). Validation and Performance Analysis of Binary Logistic Regression Model.
  51. Requena, T., & Velasco, M. (2021). The human microbiome in sickness and in health. Rev Clin Esp (Barc), 221(4), 233-240. https://doi.org/10.1016/j.rceng.2019.07.018
  52. Saboo, K., Petrakov, N. V., Shamsaddini, A., Fagan, A., Gavis, E. A., Sikaroodi, M., McGeorge, S., Gillevet, P. M., Iyer, R. K., & Bajaj, J. S. (2022). Stool microbiota are superior to saliva in distinguishing cirrhosis and hepatic encephalopathy using machine learning. J Hepatol, 76(3), 600-607. https://doi.org/10.1016/j.jhep.2021.11.011
  53. Sathiyanarayanan, P., Pavithra, S., M. Sai, S., & Makeswari, M. (2019, 29-30 March 2019). Identification of Breast Cancer Using The Decision Tree Algorithm. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)., https://doi.org/10.1109/ICSCAN.2019.8878757
  54. Sharma, H., & Kumar, S. (2016). A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR), 5(4), 2094-2097.
  55. Srivastava, T. (2024). Retrieved 06.12.2024 from https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/
  56. Tallón-Ballesteros, A. J., Fong, S., & Leal-Díaz, R. (2019). Does the order of attributes play an important role in classification? Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14. https://doi.org/10.1007/978-3-030-29859-3_32
  57. Teixeira, M., Silva, F., Ferreira, R. M., Pereira, T., Figueiredo, C., & Oliveira, H. P. (2024). A review of machine learning methods for cancer characterization from microbiome data. NPJ Precision Oncology, 8(1), 123. https://doi.org/10.1038/s41698-024-00617-7
  58. Timsit, J. F., Ruppe, E., Barbier, F., Tabah, A., & Bassetti, M. (2020). Bloodstream infections in critically ill patients: an expert statement. Intensive Care Med, 46(2), 266-284. https://doi.org/10.1007/s00134-020-05950-6
  59. Topçuoğlu, B. D., Lapp, Z., Sovacool, K. L., Snitkin, E., Wiens, J., & Schloss, P. D. (2021). mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. J Open Source Softw, 6(61). https://doi.org/10.21105/joss.03073
  60. Topçuoğlu, B. D., Lesniak, N. A., Ruffin IV, M. T., Wiens, J., & Schloss, P. D. (2020). A framework for effective application of machine learning to microbiome-based classification problems. MBio, 11(3). https://doi.org/10.1128/mbio.00434-20
  61. Tsafarova, B., Hodzhev, Y., Yordanov, G., Tolchkov, V., Kalfin, R., & Panaiotov, S. (2022). Morphology of blood microbiota in healthy individuals assessed by light and electron microscopy. Front Cell Infect Microbiol, 12, 1091341. https://doi.org/10.3389/fcimb.2022.1091341
  62. Wang, X.-W., & Liu, Y.-Y. (2020). Comparative study of classifiers for human microbiome data. Medicine in microecology, 4, 100013. https://doi.org/10.1016/j.medmic.2020.100013
  63. Wilhelm, R. C., van Es, H. M., & Buckley, D. H. (2022). Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biology and Biochemistry, 164, 108472. https://doi.org/10.1016/j.soilbio.2021.108472
  64. Wu, Q., & Zhou, D.-X. (2006). Analysis of support vector machine classification. Journal of Computational Analysis & Applications, 8(2).
  65. Zheng, D., Liwinski, T., & Elinav, E. (2020). Interaction between microbiota and immunity in health and disease. Cell Res, 30(6), 492-506. https://doi.org/10.1038/s41422-020-0332-7
  66. D'Elia, D., Truu, J., Lahti, L., Berland, M., Papoutsoglou, G., Ceci, M., Zomer, A., Lopes, M. B., Ibrahimi, E., Gruca, A., Nechyporenko, A., Frohme, M., Klammsteiner, T., Pau, E. C. S., Marcos-Zambrano, L. J., Hron, K., Pio, G., Simeon, A., Suharoschi, R.,. Claesson, M. J. (2023). Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front Microbiol, 14, 1257002. https://doi.org/10.3389/fmicb.2023.1257002
  67. Kouchaki, S., Yang, Y., Lachapelle, A., Walker, T. M., Walker, A. S.,, C. C., Peto, T. E. A., Crook, D. W., & Clifton, D. A. (2020). Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking [Original Research]. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.00667