High-throughput genome-wide molecular assays, which probe cellular networks from
different perspectives, have become central to molecular biology. Probabilistic graphical …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

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 …

Often considered more as an art than a science, the field of clustering has been dominated
by learning through examples and by techniques chosen almost through trial-and-error …

Motivation: Clustering is a useful exploratory technique for the analysis of gene expression
data. Many different heuristic clustering algorithms have been proposed in this context …

Cluster analysis aims at identifying groups of similar objects, and helps to discover
distribution of patterns and interesting correlations in large data sets. Especially, fuzzy …

Motivation: The integration of multiple datasets remains a key challenge in systems biology
and genomic medicine. Modern high-throughput technologies generate a broad array of …

Clustering is commonly used for analyzing gene expression data. Despite their successes,
clustering methods suffer from a number of limitations. First, these methods reveal …

The availability of whole genome sequences and high-throughput genomic assays opens
the door for in silico analysis of transcription regulation. This includes methods for …

Motivation: Hierarchical clustering is one of the major analytical tools for gene expression
data from microarray experiments. A major problem in the interpretation of the output from …