Computational Bioinformatics & Bio-imaging Laboratory (CBIL)


 
 

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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.

 

 

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