Current Bioinformatics

Yi-Ping Phoebe Chen
Department of Computer Science and Information Technology
La Trobe University
Melbourne
Australia

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Current Development and Review of Dynamic Bayesian Network-Based Methods for Inferring Gene Regulatory Networks from Gene Expression Data

Author(s): Sigeru Omatu, Yee Wen Choon, Chuii Khim Chong, Safaai Deris, Mohd Saberi Mohamad, Lian En Chai.

Abstract:

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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Article Details

VOLUME: 9
ISSUE: 5
Year: 2014
Page: [531 - 539]
Pages: 9
DOI: 10.2174/1574893609666140421210333
Price: $58