title: Comparison of classification methods that combine clinical data and high-dimensional mass spectrometry data.
abstract: BACKGROUND
  The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. Technologies like mass spectrometry are commonly being used in proteomic research. Mass spectrometry signals show the proteomic profiles of the individuals under study at a given time. These profiles correspond to the recording of a large number of proteins, much larger than the number of individuals. These variables come in addition to or to complete classical clinical variables. The objective of this study is to evaluate and compare the predictive ability of new and existing models combining mass spectrometry data and classical clinical variables. This study was conducted in the context of binary prediction.
  
  
  RESULTS
  To achieve this goal, simulated data as well as a real dataset dedicated to the selection of proteomic markers of steatosis were used to evaluate the methods. The proposed methods meet the challenge of high-dimensional data and the selection of predictive markers by using penalization methods (Ridge, Lasso) and dimension reduction techniques (PLS), as well as a combination of both strategies through sparse PLS in the context of a binary class prediction. The methods were compared in terms of mean classification rate and their ability to select the true predictive values. These comparisons were done on clinical-only models, mass-spectrometry-only models and combined models.
  
  
  CONCLUSIONS
  It was shown that models which combine both types of data can be more efficient than models that use only clinical or mass spectrometry data when the sample size of the dataset is large enough.
authors.affiliation: Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. caroline.truntzer@clipproteomic.fr.|University of Burgundy, Dijon, 21000, France. caroline.truntzer@clipproteomic.fr.
authors.forename: Caroline
authors.initials: C
authors.lastname: Truntzer
authors.affiliation: Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. elise.mostacci@clipproteomic.fr.|Institut de Mathématiques de Bourgogne, UMR CNRS 5584, Dijon, 21000, France. elise.mostacci@clipproteomic.fr.|University of Burgundy, Dijon, 21000, France. elise.mostacci@clipproteomic.fr.
authors.forename: Elise
authors.initials: E
authors.lastname: Mostacci
authors.affiliation: Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. aline.jeannin@clipproteomic.fr.|University of Burgundy, Dijon, 21000, France. aline.jeannin@clipproteomic.fr.
authors.forename: Aline
authors.initials: A
authors.lastname: Jeannin
authors.affiliation: Service Endocrinologie, Centre Hospitalier Universitaire, Dijon, 21000, France. jean-michel.petit@chu-dijon.fr.
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Petit
authors.affiliation: Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. patrick.ducoroy@clipproteomic.fr.|University of Burgundy, Dijon, 21000, France. patrick.ducoroy@clipproteomic.fr.
authors.forename: Patrick
authors.initials: P
authors.lastname: Ducoroy
authors.affiliation: Institut de Mathématiques de Bourgogne, UMR CNRS 5584, Dijon, 21000, France. herve.cardot@clipproteomic.fr.|University of Burgundy, Dijon, 21000, France. herve.cardot@clipproteomic.fr.
authors.forename: Hervé
authors.initials: H
authors.lastname: Cardot
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D002965:Classification / Q000379:methods*; D005234:Fatty Liver / Q000097:blood* / Q000175:diagnosis; D006801:Humans; D040901:Proteomics / Q000379:methods*; D018401:Sample Size; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D015415:Biomarkers