How best to summarize large and complex datasets is a problem that arises in many areas of science. We approach it from the point of view of seeking data summaries that minimize the average squared error of the posterior distribution for a parameter of interest under approximate Bayesian...

The genetic control of a complex trait can be studied by testing and mapping the genotypes of the underlying quantitative trait loci (QTLs) through their associations with observable marker genotypes. All existing statistical methods for QTL mapping assume an equilibrium population, allowing...

Statistical methods generating sparse models are of great value in the gene expression field, where the number of covariates (genes) under study moves about the thousands while the sample sizes seldom reach a hundred of individuals. For phenotype classification, we propose different lasso...

Multiallelic markers, such as microsatellites, provide a powerful tool for studying the genetic structure and organization of an outcrossing population. However, statistical methods of analyzing multiallelic markers in current literature are limited in scope due to the complexity of the multiple...

Partial least squares (PLS) is a well known dimension reduction method which has been recently adapted for high dimensional classification problems in genome biology. We develop sparse versions of the recently proposed two PLS-based classification methods using sparse partial least squares...

There are a number of common human diseases for which the genetic component may include an epistatic interaction of multiple genes. Detecting these interactions with standard statistical tools is difficult because there may be an interaction effect, but minimal or no main effect....

High density tiling arrays are an effective strategy for genome-wide identification of transcription factor binding regions. Sliding window methods that calculate moving averages of log ratios or t-statistics have been useful for the analysis of tiling array data. Here, we present a method that...

Probabilistic mixture models provide a popular approach to cluster noisy gene expression data for exploring gene function. Since gene expression data obtained from microarray experiments are often associated with significant sources of technical and biological noise, replicated experiments are...

We propose a new approach for clustering DNA features using array CGH data from multiple tumor samples. We distinguish data-collapsing (joining contiguous DNA clones or probes with extremely similar data into regions) from clustering (joining contiguous, correlated regions based on a maximum...

Characterizing dynamic gene expression pattern and predicting patient outcome is now significant and will be of more interest in the future with large scale clinical investigation of microarrays. However, there is currently no method that has been developed for prediction of patient outcome...