TY - JOUR
T1 - Current development and review of dynamic bayesian network-based methods for inferring gene regulatory networks from gene expression data
AU - Chai, Lian En
AU - Mohamad, Mohd Saberi
AU - Deris, Safaai
AU - Chong, Chuii Khim
AU - Choon, Yee Wen
AU - Omatu, Sigeru
N1 - Publisher Copyright:
© 2014 Bentham Science Publishers.
PY - 2014/3/1
Y1 - 2014/3/1
N2 - 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.
AB - 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.
KW - Dynamic bayesian network
KW - Gene regulatory networks
KW - Network inference
KW - Time-series gene expression data
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U2 - 10.2174/1574893609666140421210333
DO - 10.2174/1574893609666140421210333
M3 - Article
AN - SCOPUS:84911442839
SN - 1574-8936
VL - 9
SP - 531
EP - 539
JO - Current Bioinformatics
JF - Current Bioinformatics
IS - 5
ER -