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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)
Clustering is a common methodology for the analysis of array data, and many research laboratories are generating array data with repeated measurements. We evaluated several clustering algorithms that incorporate repeated measurements, and show that algorithms that take advantage of repeated measurements yield more accurate and more stable clusters. In(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)
Rearrangements involving the RET gene are common in radiation-associated papillary thyroid cancer (PTC). The RET/PTC1 type of rearrangement is an inversion of chromosome 10 mediated by illegitimate recombination between the RET and the H4 genes, which are 30 megabases apart. Here we ask whether despite the great linear distance between them, RET and H4(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)
In the paper, we reported results on the yeast galactose data after imputing missing values. Here, we show results on the yeast galactose data without imputing any missing values, i.e., we ignore the corresponding values when computing the similarity measures. Since the current implementations of the model-based approaches cannot deal with missing data(More)
BACKGROUND Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental technologies, gene expression microarrays and Chromatin Immuno-Precipitation on Chip (ChIP-chip), are capable of producing data informative about expression regulatory mechanism on(More)
Nutritional interventions are important alternatives for reducing the prevalence of many chronic diseases. Soy is a good source of protein that contains isoflavones, including genistein and daidzein, and may alter the risk of obesity, Type 2 diabetes, osteoporosis, cardiovascular disease, and reproductive cancers. We have shown previously in nonhuman(More)
BACKGROUND Cluster analysis is often used to infer regulatory modules or biological function by associating unknown genes with other genes that have similar expression patterns and known regulatory elements or functions. However, clustering results may not have any biological relevance. RESULTS We applied various clustering algorithms to microarray(More)
BACKGROUND The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for(More)