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Malaysian Journal of Medical Sciences
School of Medical Sciences, Universiti Sains Malaysia
ISSN: 1394-195X
Vol. 21, No. 2, 2014, pp. 20-27
Bioline Code: mj14017
Full paper language: English
Document type: Research Article
Document available free of charge

Malaysian Journal of Medical Sciences, Vol. 21, No. 2, 2014, pp. 20-27

 en Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data
CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai & ILLIAS, Rosli Md

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 check for this species in other resources 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 check for this species in other resources dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset).
Conclusion: 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.

Keywords
Bayesian method; DNA microarrays; gene expression; gene regulatory networks; gene expression regulation

 
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Alternative site location: http://www.medic.usm.my/publication/mjms/

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