Classification of the urinary metabolome using machine learning and potential applications to diagnosing interstitial cystitis
Main Article Content
Keywords
interstitial cystitis, biomarker, urine, metabolomics, machine learning, artificial algorithm
Abstract
With the advent of artificial intelligence (AI) in biostatistical analysis and modeling, machine learning can potentially be applied into developing diagnostic models for interstitial cystitis (IC). In the current clinical setting, urologists are dependent on cystoscopy and questionnaire-based decisions to diagnose IC. This is a result of a lack of objective diagnostic molecular biomarkers. The purpose of this study was to develop a machine learning-based method for diagnosing IC and assess its performance using metabolomics profiles obtained from a prior study. To develop the machine learning algorithm, two classification methods, support vector machine (SVM) and logistic regression (LR), set at various parameters, were applied to 43 IC patients and 16 healthy controls. There were 3 measures used in this study, accuracy, precision (positive predictive value), and recall (sensitivity). Individual precision and recall (PR) curves were drafted. Since the sample size was relatively small, complicated deep learning could not be done. We achieved a 76%–86% accuracy with leave-one-out cross validation depending on the method and parameters set. The highest accuracy achieved was 86.4% using SVM with a polynomial kernel degree set to 5, but a larger area under the curve (AUC) from the PR curve was achieved using LR with a l1-norm regularizer. The AUC was greater than 0.9 in its ability to discriminate IC patients from controls, suggesting that the algorithm works well in identifying IC, even when there is a class distribution imbalance between the IC and control samples. This finding provides further insight into utilizing previously identified urinary metabolic biomarkers in developing machine learning algorithms that can be applied in the clinical setting.
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References
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3. Hanno PM, Burks DA, Clemens JQ, Dmochowski RR, Erickson D, Fitzgerald MP, et al. AUA guideline for the diagnosis and treatment of interstitial cystitis/bladder pain syndrome. J Urol. 2011;185(6):2162-70. PMID: 21497847
4. Urinology Think Tank Writing G. Urine: Waste product or biologically active tissue? Neurourol Urodyn. 2018;37(3):1162-8. PMID: 29464759
5. Wen H, Lee T, You S, Park SH, Song H, Eilber KS, et al. Urinary metabolite profiling combined with computational analysis predicts interstitial cystitis-associated candidate biomarkers. J Proteome Res. 2015;14(1):541-8. PMID: 25353990
6. Kind T, Cho E, Park TD, Deng N, Liu Z, Lee T, et al. Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis. Sci Rep. 2016;6:39227. PMID: 27976711
7. Shahid M, Lee MY, Yeon A, Cho E, Sairam V, Valdiviez L, et al. Menthol, a unique urinary volatile compound, is associated with chronic inflammation in interstitial cystitis. Sci Rep. 2018;8(1):10859. PMID: 30022124
8. Cahan EM, Hernandez-Boussard T, Thadaney-Israni S, Rubin DL. Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit Med. 2019;2:78. PMID: 31453373
9. Tolles J, Meurer WJ. Logistic regression: relating patient characteristics to outcomes. Jama. 2016;316(5):533-4. PMID: 27483067
10. Cortes C, Vapnik V. Support-vector networks. Machine learning. 1995;20(3):273-97.
11. Platt J. Sequential minimal optimization: A fast algorithm for training support vector machines. 1998.
12. Lin Y, Lee Y, Wahba G. Support vector machines for classification in nonstandard situations. Machine learning. 2002;46(1-3):191-202.
13. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological). 1996;58(1):267-88.
14. Ng AY, editor Feature selection, L 1 vs. L 2 regularization, and rotational invariance. Proceedings of the twenty-first international conference on Machine learning; 2004.
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18. Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol. 2019;15(7):e1007084. PMID: 31295267
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20. Cuperlovic-Culf M. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites. 2018;8(1). PMID: 29324649
21. Angermueller C, Parnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12(7):878. PMID: 27474269
22. Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017;18(5):851-69. PMID: 27473064
23. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463-77. PMID: 30976107
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28. Wong NC, Lam C, Patterson L, Shayegan B. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int. 2019;123(1):51-7. PMID: 29969172
29. Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019;10(1):5221. PMID: 31745082
30. Saeed K, Rahkama V, Eldfors S, Bychkov D, Mpindi JP, Yadav B, et al. Comprehensive Drug Testing of Patient-derived Conditionally Reprogrammed Cells from Castration-resistant Prostate Cancer. Eur Urol. 2017;71(3):319-27. PMID: 27160946
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Published
Jun 2, 2020
Article Details
How to Cite
Tong, F., Shahid, M., Jin, P., Jung, S., Kim, W. H., & Kim, J. (2020). Classification of the urinary metabolome using machine learning and potential applications to diagnosing interstitial cystitis. Bladder, 7(2), e43. https://doi.org/10.14440/bladder.2020.815
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