Inferring E. coli SOS response pathway from gene expression data using IST-DBN with time lag estimation

Lian En Chai, Mohd Saberi Mohamad, Safaai Deris, Chuii Khim Chong, Yee Wen Choon

Research output: Chapter in Book/Report/Conference proceedingChapter


Driven to discover the vast information and comprehend the fundamental mechanism of gene regulations, gene regulatory networks (GRNs) inference from gene expression data has gathered the interests of many researchers which is otherwise unfeasible in the past due to technology constraint. The dynamic Bayesian network (DBN) has been widely used to infer GRNs as it is capable of handling time-series gene expression data and feedback loops. However, the frequently occurred missing values in gene expression data, the incapability to deal with transcriptional time lag, and the excessive computation time triggered by the large search space, are attributed to restraint the effectiveness of DBN in inferring GRNs from gene expression data. This paper proposes a DBN-based model (IST-DBN) with missing values imputation, potential regulators selection, and time lag estimation to address these problems. To assess the performance of IST-DBN, we applied the model on the E. coli SOS response pathway time-series expression data. The experimental results showed IST-DBN has higher accuracy and faster computation time in recognising gene-gene relationships when compared with existing DBN-based model and conventional DBN. We also believe that the ensuing networks from IST-DBN are applicable as a common framework for prospective gene intervention study.

Original languageEnglish
Title of host publicationAdvances in Biomedical Infrastructure 2013
Subtitle of host publicationProceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013)
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783642371363
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • Dynamic bayesian network
  • Gene regulatory networks
  • Missing values imputation
  • Network inference
  • Time-series gene expression data

ASJC Scopus subject areas

  • Artificial Intelligence


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