In the post-genome era, designing and conducting novel experiments have become increasingly common for
modern researchers. However, the major challenge faced by researchers is surprisingly not the complexity in designing
new experiments or obtaining the data generated from the experiments, but instead it is the huge amount of data to be
processed and analyzed in the quest to produce meaningful information and knowledge. Gene regulatory network (GRN)
inference from gene expression data is one of the common examples of such challenge. Over the years, GRN inference
has witnessed a number of transitions, and an increasing amount of new computational and statistical-based methods have
been applied to automate the procedure. One of the widely used approaches for GRN inference is the dynamic Bayesian
network (DBN). In this review paper, we first discuss the evolution of molecular biology research from reductionism to
holism. This is followed by a brief insight on various computational and statistical methods used in GRN inference before
focusing on reviewing the current development and applications of DBN-based methods.
Keywords: Dynamic bayesian network, gene regulatory networks, network inference, time-series gene expression data.
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