title: Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA.
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
authors.forename: Tom
authors.initials: T
authors.lastname: Abbott
authors.forename: David
authors.initials: D
authors.lastname: Fishman
authors.forename: Walter
authors.initials: W
authors.lastname: McMurray
authors.forename: Gil
authors.initials: G
authors.lastname: Mor
authors.forename: Kathryn
authors.initials: K
authors.lastname: Stone
authors.forename: David
authors.initials: D
authors.lastname: Ward
authors.forename: Kenneth
authors.initials: K
authors.lastname: Williams
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis / Q000097:blood*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000032:analysis / Q000097:blood; D010051:Ovarian Neoplasms / Q000097:blood* / Q000145:classification* / Q000175:diagnosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins