Inferring gene regulatory networks from perturbed gene expression data using a dynamic Bayesian network with a Markov Chain Monte Carlo algorithm

Swee Thing Low, Mohd Saberi Mohamad, Sigeru Omatu, Lian En Chai, Safaai Deris, Michifumi Yoshioka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In understanding the biological role of genes and gene products, the analysis of gene regulatory functions is important. Computational methods for running gene regulatory networks inference have its own limitations. For instance, Bayesian Network and Boolean Network are unable to model the cyclic relationship and the interaction uncertainties, which are important elements in the biological networks. Hence, Dynamic Bayesian Network (DBN) was employed in this research to overcome the limitations. Even though DBN performs better than other methods in term of accuracy, its prediction accuracy is still considered low. Due to this, optimization algorithm is necessary to improve the accuracy performance. Therefore, this research is concerned on inferring gene regulatory networks using DBN with Markov Chain Monte Carlo (MCMC) algorithm for the improvement of prediction accuracy. The research results were compared with the results from previous works in terms of accuracy, sensitivity and specificity. Experimental results show that our proposed approach (DBN with MCMC) is better than existing work in term of prediction accuracy.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
EditorsYasuo Kudo, Shusaku Tsumoto
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages179-184
Number of pages6
ISBN (Electronic)9781479954643
DOIs
Publication statusPublished - Dec 11 2014
Externally publishedYes
Event2014 IEEE International Conference on Granular Computing, GrC 2014 - Hokkaido, Japan
Duration: Oct 22 2014Oct 24 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014

Conference

Conference2014 IEEE International Conference on Granular Computing, GrC 2014
Country/TerritoryJapan
CityHokkaido
Period10/22/1410/24/14

Keywords

  • Bioinformatics
  • Dynamic Bayesian Network
  • Gene Expression Data
  • Gene Regulatory Network Inference
  • Markov Chain Monte Carlo
  • Perturbation

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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