Knowledge-based variable selection for learning rules from proteomic data

BMC Bioinformatics. 2009 Sep 17;10 Suppl 9(Suppl 9):S16. doi: 10.1186/1471-2105-10-S9-S16.


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.

Conclusion: 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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Pattern Recognition, Automated / methods
  • Proteome / analysis*
  • Proteomics / methods*


  • Proteome