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

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

Volume: 9 Issue: 5

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

Affiliation:

Keywords: Dynamic bayesian network, gene regulatory networks, network inference, time-series gene expression data.

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.

Cite this article as:

Chai En Lian, Mohamad Saberi Mohd, Deris Safaai, Chong Khim Chuii, Choon Wen Yee and Omatu Sigeru, Current Development and Review of Dynamic Bayesian Network-Based Methods for Inferring Gene Regulatory Networks from Gene Expression Data, Current Bioinformatics 2014; 9 (5) . https://dx.doi.org/10.2174/1574893609666140421210333