Ensemble of Elman neural networks and support vector machines for reverse engineering of gene regulatory networks

S. I. Ao, V. Palade

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

In this study, recurrent Elman neural networks (ENNs) and support vector machines (SVMs) have been used for temporal modeling of microarray continuous time series data. An ensemble of ENN and SVM models is proposed to further improve the prediction accuracy of the individual models. The prediction results on the simulated non-stationary datasets and the real biological datasets outperform the results of the other existing approaches. In order to provide the neural networks with explanation capabilities, a pedagogical rule extraction technique has been proposed to infer the output of our proposed ensemble system. The proposed pedagogical rule extraction technique is a two-step test of causality and Pearson correlation for the network inference between the causal gene expression inputs and their predicted outputs. The results of the network inference demonstrate that the gene regulatory network can be reconstructed satisfactorily with the proposed approach.

Original languageEnglish
Pages (from-to)1718-1726
Number of pages9
JournalApplied Soft Computing Journal
Volume11
Issue number2
DOIs
Publication statusPublished - Mar 2011
Externally publishedYes

Keywords

  • Classifier ensembles
  • Elman neural network
  • Gene regulatory network
  • Reverse engineering
  • Support vector machines

ASJC Scopus subject areas

  • Software

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