The likelihood–free sequential Approximate Bayesian Computation (ABC) algorithms are increasingly popular inference tools for complex biological models. Such algorithms proceed by constructing a succession of probability distributions over the parameter space conditional upon the simulated...

Pooling is an important resource in microarray gene expression experiments. Due to restrictions imposed by the statistical analysis it is widespread practice to employ a fixed pool size over the whole experiment. But this limits the efficient use of experimental material. In this paper we...

Regulatory networks consist of genes encoding transcription factors (TFs) and the genes they activate or repress. Various types of systems of ordinary differential equations (ODE) have been proposed to model these networks, ranging from linear to Michaelis-Menten approaches. In practice, a...

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external...

A general method for obtaining highly efficient factorial designs of relatively small sizes is developed for cDNA microarray experiments. It allows the main effects and interactions to be of possibly unequal importance. First, the approximate theory is employed to get an optimal design measure...

In the design of microarray or next-generation sequencing experiments it is crucial to choose the appropriate number of biological replicates. As often the number of differentially expressed genes and their effect sizes are small and too few replicates will lead to insufficient power to detect...

Genomics studies frequently involve clustering of molecular data to identify groups, but common clustering methods such as K-means clustering and hierarchical clustering do not determine the number of clusters. Methods for estimating the number of clusters typically focus on identifying the...

RNA sequencing (RNA-Seq) is the current method of choice for characterizing transcriptomes and quantifying gene expression changes. This next generation sequencing-based method provides unprecedented depth and resolution. The negative binomial (NB) probability distribution has been shown to be a...

Recent technology advances make it possible to collect whole-genome transcription factor binding (TFB) profiles from multiple species through the ChIP-Seq data. This provides rich information to understand TFB evolution. However, few rigorous statistical models are available to infer TFB...

Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian...