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Bioinformatics Advance Access published online on October 31, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl553
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 18, 2006
Revised October 17, 2006
Accepted October 24, 2006

Article

Dependence network modeling for biomarker identification

Peng Qiu 1 *, Jane Wang 2, K. J. Ray Liu 1, Zhang-Zhi Hu 3, and Cathy H. Wu 3

1 Department of Electrical and Computer Engineering, University of Maryland, College Park, USA
2 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
3 Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington DC, USA

* To whom correspondence should be addressed.
Peng Qiu, E-mail: qiupeng{at}umd.edu


  Abstract

Motivation: Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.

Results: Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction.

Availability: See supplements: dsplab.eng.umd.edu/~genomics/dependencenetwork/.

Associate Editor: Martin Bishop
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