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MOTIVATION The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. (More)
MOTIVATION Identifying patterns of co-expression in microarray data by cluster analysis has been a productive approach to uncovering molecular mechanisms underlying biological processes under investigation. Using experimental replicates can generally improve the precision of the cluster analysis by reducing the experimental variability of measurements. In(More)
MOTIVATION The elucidation of biological pathways enriched with differentially expressed genes has become an integral part of the analysis and interpretation of microarray data. Several statistical methods are commonly used in this context, but the question of the optimal approach has still not been resolved. RESULTS We present a logistic regression-based(More)
MOTIVATION Identifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional(More)
MOTIVATION Functional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of 'significant' and 'non-significant' genes based on ad hoc significance cutoffs. This can lead(More)
Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are involved. Traditional model-based clustering approaches as well as some recently developed model-based mining approaches for integrating genomic and functional genomic data rely on(More)
Unsupervised learning methods have been tremendously successful in extracting knowledge from genomics data generated by high throughput experimental assays. However, analysis of each dataset in isolation without incorporating potentially informative prior knowledge is limiting the utility of such procedures. Here we present a novel probabilistic model and(More)
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