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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2009-9-18
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pubmed:abstractText |
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.
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pubmed:grant |
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pubmed:commentsCorrections |
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:chemical |
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pubmed:status |
MEDLINE
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pubmed:issn |
1471-2105
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
10 Suppl 9
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
S16
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pubmed:meshHeading |
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pubmed:year |
2009
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pubmed:articleTitle |
Knowledge-based variable selection for learning rules from proteomic data.
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pubmed:affiliation |
Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu
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pubmed:publicationType |
Journal Article,
Research Support, N.I.H., Extramural
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