Computational
Decomposition of Composite Molecular Signatures (EB000830)
The
long-term goal of the proposed work is to develop and validate
novel and effective computational tools to decompose composite
molecular signatures in microarray and imaging studies: tissue
heterogeneity correction (THC) and iterative selection-normalization
(ISN).
The
technology development is driven by the hypotheses that: (1) accurate
separation of the gene expression profiles of mixed cell populations
(e.g., malignant/stromal cells), and/or (2) accurate normalization
of the gene expression profiles across multiple phenotypes, will
improve the sensitivity and specificity for the measurement of
molecular signatures and for early detection and diagnosis of
diseases.
The
R21 phase will focus on: (1) develop and test computational source separation
(CSS) method, using well-established cell line microarray experiment,
to extract the gene expression profile of targeted cells from
observed mixtures. (2) develop and test the ISN based cross-phenotype
normalization (CPN) method to simultaneously identify constantly-expressed
genes and normalize gene expression profiles by linear regressions.
The R33 phase will focus on: (1) apply and test the performance
of CSS method, using gene expression datasets derived by microarrays
from multiple real biopsy specimens of solid tumors. (2) develop
and test a population-based ISN-CPN algorithm, using multi-phenotype
samples, to simultaneously identify constantly-expressed genes
and normalize gene expression profiles by applicable nonlinear
regressions. (3) apply and test the CSS method, using in-vivo
molecular imaging datasets, to separate specific and nonspecific
bindings with improved signal-to-noise ratio.
Copyright
©2004, Computational Bioinformatics and Bioimaging Laboratory
(CBIL), Alexandria Research Institute, Virginia Tech. Jointly
with The Catholic University of America.
Last
Updated: 03/22/2004. Suggestions/Comments
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