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Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model

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Part of the Proceedings in Information and Communications Technology book series (PICT,volume 6)

Abstract

In this work we used the decoupled version of the recurrent neural network (RNN) model for gene network inference from gene expression data. In the decoupled version, the global problem of estimating the full set of parameters for the complete network is divided into several sub-problems each of which corresponds to estimating the parameters associated with a single gene. Thus, the decoupling of the model decreases the problem dimensionality and makes the reconstruction of larger networks more feasible from the point of algorithmic perspective. We applied a well established evolutionary algorithm called differential evolution for inferring the underlying network structure as well as the regulatory parameters. We investigated the effectiveness of the reconstruction mechanism in analyzing the gene expression data collected from both synthetic and real gene networks. The proposed method was successful in inferring important gene interactions from expression profiles.

Keywords

  • Recurrent Neural Network model
  • gene network reconstruction
  • decoupled RNN
  • differential evolution

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Noman, N., Palafox, L., Iba, H. (2013). Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model. In: Suzuki, Y., Nakagaki, T. (eds) Natural Computing and Beyond. Proceedings in Information and Communications Technology, vol 6. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54394-7_8

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  • DOI: https://doi.org/10.1007/978-4-431-54394-7_8

  • Publisher Name: Springer, Tokyo

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