Knowledge-based variable selection for learning rules from proteomic data.
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.
Lustgarten
Jonathan L
JL
Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu
Visweswaran
Shyam
S
Bowser
Robert P
RP
Hogan
William R
WR
Gopalakrishnan
Vanathi
V
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
D020543:Proteome