Gene expression data from microarray experiments have been studied using several statistical models. Significance Analysis of Microarrays (SAM), for example, has proved to be useful in analyzing microarray data. In the spirit of the SAM procedures, we develop permutation based rank-tests for...

In this paper biological sequences are modelled by stationary ergodic sequences. A new family of statistical tests to characterize the randomness of the inputs is proposed and analyzed. Tests for independence and for the determination of the appropriate order of a Markov chain are constructed...

We examine the application of statistical model selection methods to reverse-engineering the control of galactose utilization in yeast from DNA microarray experiment data. In these experiments, relationships among gene expression values are revealed through modifications of galactose sugar level...

A combined score test for association and linkage analysis is introduced, based on a biologically plausible model with association between markers and causal genes and penetrance between phenotypes and the causal gene. The test is based on a retrospective likelihood of marker data given...

The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we...

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose...

Recent analytic and technological breakthroughs have set the stage for genome-wide linkage disequilibrium studies to map disease-susceptibility variants. This paper discusses a probabilistic methodology for making disease-mapping inferences in large-scale case-control genetic studies. The...

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a...

Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can...

We present a novel, cost efficient two-phase design for predictive clinical gene expression studies: early marker panel determination (EMPD). In Phase-1, genome-wide microarrays are used only for a small number of individual patient samples. From this Phase-1 data a panel of marker genes is...