High throughput biological data need to be processed, analyzed, and interpreted to address
problems in life sciences. Bioinformatics, computational biology, and systems biology deal …

Background The analysis of biological networks has become a major challenge due to the
recent development of high-throughput techniques that are rapidly producing very large data …

The graph clustering problem has become highly relevant due to the growing interest of
several research communities in social networks and their possible applications …

Background A wealth of clustering algorithms has been applied to gene co-expression
experiments. These algorithms cover a broad range of approaches, from conventional …

Clustering is an unsupervised learning technique in that there is no explicit demarcation of
data as training and test data. Clustering aims to group related records by measuring …

Background Biclustering is a powerful technique for identification of co-expressed gene
groups under any (unspecified) substantial subset of given experimental conditions, which …

Toxicology and pharmaceutical research is increasingly making use of high throughout-
screening (HTS) methods to assess the effects of chemicals on molecular pathways, cells …

The clustering problem consists in finding patterns in a data set in order to divide it into
clusters with high within-cluster similarity. This paper presents the study of a problem, here …

Recent technologies and tools generated excessive data in bioinformatics domain. For
example, microarrays measure expression levels of ten thousands of genes simultaneously …

Data on a wide-range of bio-chemical phenomena is often highly non-linear. Due to this
characteristic, data analysis tasks, such as clustering can become non-trivial. In recent …