title: Investigating the effects of imputation methods for modelling gene networks using a dynamic bayesian network from gene expression data.
abstract: BACKGROUND
  Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN).
  
  
  METHODS
  In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error.
  
  
  RESULTS
  We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset).
  
  
  CONCLUSIONS
  The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes.
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Lian En
authors.initials: LE
authors.lastname: Chai
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Chow Kuan
authors.initials: CK
authors.lastname: Law
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Mohd Saberi
authors.initials: MS
authors.lastname: Mohamad
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Chuii Khim
authors.initials: CK
authors.lastname: Chong
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Yee Wen
authors.initials: YW
authors.lastname: Choon
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Safaai
authors.initials: S
authors.lastname: Deris
authors.affiliation: Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Rosli Md
authors.initials: RM
authors.lastname: Illias
keywords: Bayesian method; DNA microarrays; gene expression; gene expression regulation; gene regulatory networks