Recent biotechnology advances allow for multiple types of omics data, such as
transcriptomic, proteomic or metabolomic data sets to be integrated. The problem of feature …

Reverse engineering methods are typically first tested on simulated data from in silico
networks, for systematic and efficient performance assessment, before an application to real …

Many methods have been developed for statistical analysis of microbial community profiles,
but due to the complex nature of typical microbiome measurements (eg sparsity, zero …

Background One of the emerging techniques for performing the analysis of the DNA
microarray data known as biclustering is the search of subsets of genes and conditions …

Background Normalization is essential in dual-labelled microarray data analysis to remove
non-biological variations and systematic biases. Many normalization methods have been …

OBJECTIVE: DNA microarray technology yields expression profiles for thousands of genes,
in a single hybridization experiment. The quantification of the expression level is performed …

Data derived from microarray technologies are generally subject to various sources of noise
and accordingly the raw data are pre-processed before formally analysed. Data …

Microarray technology allows monitoring of gene expression profiling at the genome level.
This is useful in order to search for genes involved in a disease. The performances of the …

Motivation: Although numerous algorithms have been developed for microarray
segmentation, extensive comparisons between the algorithms have acquired far less …

Genetic regulatory networks (GRNs) are causal structures which can be represented as
large directed graphs. Their inference is a central problem in bioinformatics. Because of the …