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Knowledge-based variable selection for learning rules from proteomic data
Authors
Robert P. Bowser
Vanathi Gopalakrishnan
+3 more
William R. Hogan
Jonathan L. Lustgarten
Shyam Visweswaran
Publication date
17 September 2009
Publisher
Barrow - St. Joseph\u27s Scholarly Commons
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. 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
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Last time updated on 27/02/2021