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Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection
Joint Authors
Zhang, Yinan
Chen, Tianlu
Jia, Weiping
Wang, Congrong
Liu, Jiajian
Cao, Yu
Zhao, Aihua
Bao, Yu-Qian
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments.
Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in metabolomics, their performance including strengths and limitations in clinical data analysis has not been clear to researchers due to the lack of systematic evaluation of these tools.
In this paper we comparatively evaluated the four classifiers, PLS, SVM, LDA, and RF, in the analysis of clinical metabolomic data derived from gas chromatography mass spectrometry platform of healthy subjects and patients diagnosed with colorectal cancer, where cross-validation, R2/Q2 plot, receiver operating characteristic curve, variable reduction, and Pearson correlation were performed.
RF outperforms the other three classifiers in the given clinical data sets, highlighting its comparative advantages as a suitable classification and biomarker selection tool for clinical metabolomic data analysis.
American Psychological Association (APA)
Chen, Tianlu& Cao, Yu& Zhang, Yinan& Liu, Jiajian& Bao, Yu-Qian& Wang, Congrong…[et al.]. 2013. Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection. Evidence-Based Complementary and Alternative Medicine،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-461488
Modern Language Association (MLA)
Chen, Tianlu…[et al.]. Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection. Evidence-Based Complementary and Alternative Medicine No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-461488
American Medical Association (AMA)
Chen, Tianlu& Cao, Yu& Zhang, Yinan& Liu, Jiajian& Bao, Yu-Qian& Wang, Congrong…[et al.]. Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection. Evidence-Based Complementary and Alternative Medicine. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-461488
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-461488