Designing and conducting experiments are routine practices for modern biologists. The real
challenge, especially in the post-genome era, usually comes not from acquiring data, but …

The modeling of genetic networks especially from microarray and related data has become
an important aspect of the biosciences. This review takes a fresh look at a specific family of …

An increasing number of algorithms for biochemical network inference from experimental
data require discrete data as input. For example, dynamic Bayesian network methods and …

Constructing genetic regulatory networks is one of the most important issues in system
biology research. Yet, building regulatory models manually is a tedious task, especially …

Background Complex diseases are often difficult to diagnose, treat and study due to the
multi-factorial nature of the underlying etiology. Large data sets are now widely available …

Background Novel strategies are required in order to handle the huge amount of data
produced by microarray technologies. To infer gene regulatory networks, the first step is to …

Background Gene regulatory networks have an essential role in every process of life. In this
regard, the amount of genome-wide time series data is becoming increasingly available …

Transcriptional regulation in differentiating microorganisms is highly dynamic involving
multiple and interwinding circuits consisted of many regulatory genes. Elucidation of these …

Motivation: Gene expression profiling is an important tool for gaining insight into biology.
Novel strategies are required to analyze the growing archives of microarray data and extract …

Neuroblastoma is the most common extracranial tumor and a major cause of infant cancer
mortality worldwide. Despite its importance, little is known about its molecular mechanisms …