The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Analysis of Metabolomic Data Using Support Vector Machines
Abstract
Metabolomics is an emerging field providing insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biofluids. Multivariate statistical analysis is generally employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (for instance diseased vs healthy). Characteristic predictive models may be built based on a set of training data, and these models are subsequently used to predict whether new test data falls under a specific class. In this study, metabolomic data is obtained by doing a (1)H NMR spectroscopy on urine samples obtained from healthy subjects (male and female) and patients suffering from Streptococcus pneumoniae. We compare the performance of traditional PLS-DA multivariate analysis to support vector machines (SVMs), a technique widely used in genome studies on two case studies: (1) a case where nearly complete distinction may be seen (healthy versus pneumonia) and (2) a case where distinction is more ambiguous (male versus female). We show that SVMs are superior to PLS-DA in both cases in terms of predictive accuracy with the least number of features. With fewer number of features, SVMs are able to give better predictive model when compared to that of PLS-DA.
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Cites
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Cited by
Gromski, P. S., Muhamadali, H., Ellis, D. I., Xu, Y., Correa, E., Turner, M. L., & Goodacre, R. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.012 · Cited by 905
Chemometric methods in data processing of mass spectrometry-based metabolomics: A review
Yi, L., Dong, N., Yun, Y., Deng, B., Ren, D., Liu, S., & Liang, Y. (2016) · Analytica Chimica Acta · doi:10.1016/j.aca.2016.02.001 · Cited by 305
Breath Analysis as a Potential and Non-Invasive Frontier in Disease Diagnosis: An Overview
Pereira, J., Porto-Figueira, P., Cavaco, C., Taunk, K., Rapole, S., Dhakne, R., Nagarajaram, H., & Câmara, J. (2015) · Metabolites · doi:10.3390/metabo5010003 · Cited by 301
Analytical and statistical approaches to metabolomics research
Issaq, H. J., Van, Q. N., Waybright, T. J., Muschik, G. M., & Veenstra, T. D. (2009) · Journal of Separation Science · doi:10.1002/jssc.200900152 · Cited by 227
Metabolic Profiling Identifies Phospholipids as Potential Serum Biomarkers for Schizophrenia
Wang, D., Cheng, S. L., Fei, Q., Gu, H., Raftery, D., Cao, B., Sun, X., Yan, J., Zhang, C., & Wang, J. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2018.12.008 · Cited by 73
Metadata
- Authors
- Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-09-04
- Volume
- 80
- Issue
- 19
- Pages
- 7562-7570
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W1975775976
- Cited by
- 370
- References
- 19
- FWCI
- 6.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M. (2008). Analysis of Metabolomic Data Using Support Vector Machines. Analytical Chemistry. https://doi.org/10.1021/ac800954c
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