Bioinformatics Vol. 19 no. 13 2003
Pages 1636-1643
© 2003 Oxford University Press
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
1 Department of Epidemiology and Public Health, 2 HHMI Biopolymer/Keck Laboratory, 3 Department of Obstetrics and Gynecology, 4 Department of Genetics, Yale University School of Medicine, New Haven, CT, USA and 5 Department of OB/GYN, Northwestern University School of Medicine, Chicago, IL, USA
Received on December 18, 2002
; revised on March 3, 2003
; accepted on March 6, 2003
Motivation: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data.
Results: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.
Supplementary information: http://bioinformatics.med.yale.edu/proteomics/BioSupp1.html
Contact: hongyu.zhao{at}yale.edu
* To whom correspondence should be addressed.
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