Transcription factor binding site (TFBS) detection plays an important role in computational biology, with applications in gene finding and gene regulation. The sites are often modeled by gapless profiles, also known as position-weight matrices. Past research has focused on the significance of...

A common experimental strategy utilizing microarrays is to develop a signature of genes responding to some treatment in a model system, and then ask whether the same genes respond in an analogous way in a more natural and uncontrolled environment. In statistical terms, the question posed is...

We think of cluster analysis as class discovery. That is, we assume that there is an unknown mapping called clustering structure that assigns a class label to each observation, and the goal of cluster analysis is to estimate this clustering structure, that is, to estimate the number of clusters...

Differentiating strains of a pathogen is often central to investigating its epidemiological aspects. The genetic similarity of a group of strains can be assessed by calculating a matrix of dissimilarities from their DNA fingerprinting profiles. The mean dissimilarity for each strain across other...

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...

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...

A common practice in protein sequence alignment is to try several scoring matrices until ``something interesting'' is found. This leads to a multiple testing problem making p- and E-values hard to interpret. We focus on local alignment and propose to use logistic copula functions to model...

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...

We present a new instance of Laplace's second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric...

We present a new approach to molecular classification based on mRNA comparisons. Our method, referred to as the top-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray data for class prediction, for example to detect...