ArticleThe Malaysian journal of medical sciences : MJMS2014
Investigating the effects of imputation methods for modelling gene networks using a dynamic bayesian network from gene expression data.
Article in The Malaysian journal of medical sciences : MJMS, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data.Frontiers in systems biology · 2024Article
- An efficient ensemble method for missing value imputation in microarray gene expression data.BMC bioinformatics · 2021Article
- Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification.Frontiers in pharmacology · 2018Article
- An integrative imputation method based on multi-omics datasets.BMC bioinformatics · 2016Article
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7 authors.
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Abstract
backgroundGene 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).
methodsIn 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.
resultsWe 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).
conclusionThe 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.
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