Robust cross-race gene expression analysis
Name
Chang-2009-Robust cross-race gene expression analysis.pdf
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217.19 KB
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Checksum (MD5)
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Author(s)
Ramoni, Marco F.
Chang, Hsun-Hsien
Date Issued
January 2009
Journal
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Chang, Hsun-Hsien and M.F. Ramoni. "Robust cross-race gene expression analysis," Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pp.505-508, 19-24 (April 2009) © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
This paper develops a Bayesian network (BN) predictor to profile
cross-race gene expression data. Cross-race studies face more data
variability than single-lab studies. Our design handles this problem
by using the BN framework. In addition, unlike existing methods
that unrealistically assume independent genes, our BN approach can
capture the dependencies among genes. Existing BN algorithms in
biomedicine applications quantize data, leading to information loss;
we adopt linear Gaussian model to keep the data intact, so our resulting
model is more reliable. The application of our BN predictor to a
lung adenocarcinoma study shows high prediction accuracy, and performance
evaluation demonstrates our gene signature agreeable with
those reported in the literature. Our tool has a promising potential in
finding disease biomarkers common to multiple races.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICASSP.2009.4959631
