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Numerous algorithms exist for producing gene sets from high-throughput genomic and proteomic technologies. However, analysis of the functional significance of these groups of genes or proteins remains a big challenge. We developed a Web based system called gene-set cohesion analysis tool (GCAT) for estimating the significance level of the functional cohesion of a given gene set. The method utilizes...
In this work, we characterize genes using an oligonucleotide affymetrix gene expression dataset and propose a novel gene selection method based on samples from the posterior distributions of class-specific gene expression measures. We construct a hierarchical Bayesian framework for a random effect ANOVA model that allows us to obtain the posterior distributions of the class-specific gene expressions...
The performance of the lin-log method for modelling the glycolytic pathway in Lactococcus lactis using in vivo time-series data is investigated. The network structure of this pathway has been studied in previous reports and the authors concentrate here on the challenge of fitting the lin-log model parameters to experimental data. To calibrate the estimation methods, the performance of the lin-log...
This paper proposes a procedure for finding significance of gene ranking. The microarray data usually has a large number of genes that are not differentially expressed across multiple conditions. In microarray analysis, it is a common practice to first discard these genes as uninformative based on some filtering criterion. This filtering process results in the information loss as the uninformative...
Tumors of central nervous system (CNS) represent a unique challenge in diagnosis and treatment because of their heterogeneous phenotypic and genotypic behavior. Unambiguous characterization of these tumors is essential towards accurate prognosis and therapy. Rapid advancements in microarray technologies have made it very promising to achieve this unambiguous characterization. However, because of the...
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