Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse
engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a
promising way to characterize the global scenario of regulatory relationships between regulators and their
targets. In this review, we summarize and categorize the main frameworks and methods currently available
for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview
each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings,
and possible improvements and extensions are also clarified and commented.