We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in ...

Midbrain dopaminergic neurons are known to release dopamine from somata and/or dendrites located in the substantia nigra (SN) and the ventral tegmental area (VTA). There is considerable controversy, however, about the subcellular sites for somatodendritic dopamine storage in these regions. In the present study, we used dual-labeling electron microscopic immunocytochemistry to localize the vesic...

Static scatterplots often suffer from the overdraw problem on big datasets where object overlap causes undesirable visual clutter. The use of zooming in can help alleviate this problem. With multiple zoom levels, more screen real estate is available, allowing objects to be placed a less crowded way. We call type visualization scalable scatterplot visualizations, or SSV for short. Despite potent...

We consider Bayesian variable selection in linear regression when the relationships among a possibly large number of predictors are described by a network given a priori. A class of motivating examples is to predict some clinical outcomes with high-dimensional gene expression profiles and a gene network, for which it is assumed that the genes neighboring to each other in the network are more li...

The selection of variables in regression problems has occupied the minds of many statisticians. Several Bayesian variable selection methods have been developed, and we concentrate on the following methods: Kuo & Mallick, Gibbs Variable Selection (GVS), Stochastic Search Variable Selection (SSVS), adaptive shrinkage with Jeffreys’ prior or a Laplacian prior, and reversible jump MCMC. We review t...

Use of collaborative tools is essential in health applications, especially pathology. This paper presents the results of a huge-size image data collaborative system based on SSVS©, suitable for telepathology work, which can be implemented in any system working with large images. Quality, image stitching performance in the JPEG200 wavelet domain and image viewing performance were analyzed.