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In order to comprehensively identify genes with expression levels that correlate with survival for patients with lung adenocarcinoma, we combined data across the Harvard and Michigan studies. Two different versions of Affymetrix oligonucleotide microarrays were used in these two studies. We proposed combining arrays of different platforms by assigning weights to the expression levels of each gene...
Our goal in this work was to pool information across microarray studies conducted at different institutions using two different versions of Affymetrix chips to identify genes whose expression levels offer information on lung cancer patients’ survival above and beyond the information provided by readily available clinical covariates. We combined information across chip types by identifying “matching...
Biomarker discovery in amenably sampled body fluids has the potential to empower clinical screening programs for the early detection of disease. Liquid Chromatography interfaced to Mass Spectrometry (LC-MS) has emerged as a central technique for sensitive and automated analysis of proteins and metabolites from these clinical samples. However, the potential of LC-MS as a precise and reliable platform...
We propose a method to integrate high-dimensional genomics datasets across multiple platforms with multiple correlated imaging outcomes. This framework uses a hierarchical model to integrate biological relationships across platforms to identify genes that associate with correlated outcomes. Our two-stage hierarchical model uses the information shared across the platforms and increases the predictive...
We propose methods to integrate data across several genomic platforms using a hierarchical Bayesian analysis framework that incorporates the biological relationships among the platforms to identify genes whose expression is related to clinical outcomes in cancer. This integrated approach combines information across all platforms, leading to increased statistical power in finding these predictive genes,...
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