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. 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. © 2009 Lustgarten et al; licensee BioMed Central Ltd.
Digital Object Identifier (DOI)
Lustgarten, Jonathan L.; Visweswaran, Shyam; Bowser, Robert P.; Hogan, William R.; and Gopalakrishnan, Vanathi, "Knowledge-based variable selection for learning rules from proteomic data" (2009). Translational Neuroscience. 578.