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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Lustgarten JL,Visweswaran S,Bowser RP,Hogan WR,Gopalakrishnan Vdoi
10.1186/1471-2105-10-S9-S16subject
Has Abstractpub_date
2009-09-17 00:00:00pages
S16issn
1471-2105pii
1471-2105-10-S9-S16journal_volume
10 Suppl 9pub_type
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