title: Support vector machine approach to separate control and breast cancer serum samples.
abstract: The paper presents two analyzes of the MALDI-TOF mass spectrometry dataset. Both analyzes use the support vector machine as a tool to build a prediction model. The first analysis which is our contribution to the competition uses the given spectra data without further processing. In the second analysis, we employed an additional preprocessing step consisting of peak detection, peak alignment and feature selection based on statistical tests. The experimental results suggest that the preprocessing step with feature selection improves prediction accuracy.
authors.affiliation: Vrije Universiteit Medical Center. t.pham@vumc.nl
authors.forename: Thang V
authors.initials: TV
authors.lastname: Pham
authors.forename: Mark A
authors.initials: MA
authors.lastname: van de Wiel
authors.forename: Connie R
authors.initials: CR
authors.lastname: Jimenez
mesh_terms: D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000097:blood* / Q000145:classification / Q000175:diagnosis; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000097:blood; D040901:Proteomics / Q000706:statistics & numerical data*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000706:statistics & numerical data*
chemical_list: D009363:Neoplasm Proteins