Random matrix theory (RMT) is well suited to describing the emergent properties of systems with complex interactions amongst their constituents through their eigenvalue spectrums. Some RMT results are applied to the problem of clustering high dimensional biological data with complex dependence...

Tiling arrays make possible a large-scale exploration of the genome thanks to probes which cover the whole genome with very high density, up to 2,000,000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed...

The D2 statistic, defined as the number of matches of words of some pre-specified length k, is a computationally fast alignment-free measure of biological sequence similarity. However there is some debate about its suitability for this purpose as the variability in D2 may be dominated by the...

Inference problems with incomplete observations often aim at estimating population properties of unobserved quantities. One simple way to accomplish this estimation is to impute the unobserved quantities of interest at the individual level and then take an empirical average of the imputed...

Univariate methods have frequently been used to discover Quantitative Trait Loci for gene expression measurements, often with much success. However, correlations caused by Linkage Disequilibrium as well as chance correlations, which are functions of the large number of markers typically used in...

The integration of multiple high-dimensional data sets (omics data) has been a very active but challenging area of bioinformatics research in recent years. Various adaptations of non-standard multivariate statistical tools have been suggested that allow to analyze and visualize such data sets...

Information-theoretic metrics have been proposed for studying gene-gene and gene-environment interactions in genetic epidemiology. Although these metrics have proven very promising, they are typically interpreted in the context of communications and information transmission, diminishing their...

Gene-set analysis (GSA) aims to identify sets of differentially expressed genes by a phenotype in DNA microarray studies. Challenges occur due to the salient characteristics of the data: (1) the number of genes is far larger than the number of observations; (2) gene expression measurements,...

We propose a robust model for discovering differentially expressed genes which directly incorporates biological significance, i.e., effect dimension. Using the so-called c-fold rule, we transform the expressions into a nominal observed random variable with three categories: below a fixed lower...

Null alleles are common technical artifacts in genetic-based analysis. Powerful methods enabling their detection in either panmictic or inbred populations have been proposed. However, none of these methods appears unbiased in both types of mating systems, necessitating a priori knowledge of the...