title: Knowledge-based variable selection for learning rules from proteomic data.
abstract: BACKGROUND
  The incorporation of biological knowledge can enhance the analysis of biomedical data. We present a novel method that uses a proteomic knowledge base to enhance the performance of a rule-learning algorithm in identifying putative biomarkers of disease from high-dimensional proteomic mass spectral data. In particular, we use the Empirical Proteomics Ontology Knowledge Base (EPO-KB) that contains previously identified and validated proteomic biomarkers to select m/zs in a proteomic dataset prior to analysis to increase performance.
  
  
  RESULTS
  We show that using EPO-KB as a pre-processing method, specifically selecting all biomarkers found only in the biofluid of the proteomic dataset, reduces the dimensionality by 95% and provides a statistically significantly greater increase in performance over no variable selection and random variable selection.
  
  
  CONCLUSIONS
  Knowledge-based variable selection even with a sparsely-populated resource such as the EPO-KB increases overall performance of rule-learning for disease classification from high-dimensional proteomic mass spectra.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu
authors.forename: Jonathan L
authors.initials: JL
authors.lastname: Lustgarten
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: Robert P
authors.initials: RP
authors.lastname: Bowser
authors.forename: William R
authors.initials: WR
authors.lastname: Hogan
authors.forename: Vanathi
authors.initials: V
authors.lastname: Gopalakrishnan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
chemical_list: D020543:Proteome