Display Settings:

Format

Send to:

Choose Destination
BMC Bioinformatics. 2009 Sep 17;10 Suppl 9:S16. doi: 10.1186/1471-2105-10-S9-S16.

Knowledge-based variable selection for learning rules from proteomic data.

Author information

  • 1Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu

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.

PMID:
19761570
[PubMed - indexed for MEDLINE]
PMCID:
PMC2745687
Free PMC Article

Images from this publication.See all images (2)Free text

Figure 1
Figure 2
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk