The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

A large number of clustering approaches have been proposed for the analysis of gene
expression data obtained from microarray experiments. However, the results from the …

A versatile, platform independent and easy to use Java suite for large-scale gene
expression analysis was developed. Genesis integrates various tools for microarray data …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

Network inference, which is the reconstruction of biological networks from high-throughput
data, can provide valuable information about the regulation of gene expression in cells …

Background Data clustering analysis has been extensively applied to extract information
from gene expression profiles obtained with DNA microarrays. To this aim, existing …

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory
interactions, and apply the method to predict a large portion of the regulatory network of the …

Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …

Gene expression data hide vital information required to understand the biological process
that takes place in a particular organism in relation to its environment. Deciphering the …