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Proceedings ArticleDOI

Gene regulatory networks using bat algorithm inspired particle swarm optimization

TL;DR: A statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data and results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success.
Abstract: Here, we have proposed a statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data. The recurrent neural network formalism has been implemented to extract the underlying dynamics from time series microarray datasets accurately. The proposed swarm intelligence framework has been used for optimising the parameters of the recurrent neural network model. Preliminary research with the proposed methodology has been done on a small, artificial network and the experimental (in vivo) microarray data of the SOS DNA repair network of Escherichia coli. Results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success.
Citations
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Book ChapterDOI
26 Apr 2017
TL;DR: A novel quantum computing based technique for the reverse engineering of gene regulatory networks from time-series genetic expression datasets is proposed, suggesting that quantum computing technique significantly reduces the computational time, retaining the accuracy of the inferred gene Regulatory networks to a comparatively satisfactory level.
Abstract: The accurate reconstruction of gene regulatory networks from temporal gene expression data is crucial for the identification of genetic inter-regulations at the cellular level. This will help us to comprehend the working of living entities properly. Here, we have proposed a novel quantum computing based technique for the reverse engineering of gene regulatory networks from time-series genetic expression datasets. The dynamics of the temporal expression profiles have been modelled using the recurrent neural network formalism. The corresponding training of model parameters has been realised with the help of the proposed quantum computing methodology based concepts. This is based on entanglement and decoherence concepts. The application of quantum computing technique in this domain of research is comparatively new. The results obtained using this technique is highly satisfactory. We have applied it to a 4-gene artificial genetic network model, which was previously studied by other researchers. Also, a 10-gene and a 20-gene genetic network have been studied using the proposed technique. The obtained results suggest that quantum computing technique significantly reduces the computational time, retaining the accuracy of the inferred gene regulatory networks to a comparatively satisfactory level.

2 citations

References
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Journal ArticleDOI
TL;DR: The experimental results on two data sets studied in this paper demonstrate that the DEPSO algorithm performs better in RNN training, and the RNN-based model can provide meaningful insight in capturing the nonlinear dynamics of genetic networks and revealing genetic regulatory interactions.

129 citations


Additional excerpts

  • ...genetic algorithm (GA) [7], differential evolution [8-9], particle swarm optimisation [9-10], etc....

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Journal ArticleDOI
TL;DR: Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.
Abstract: In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

59 citations


"Gene regulatory networks using bat ..." refers background or methods or result in this paper

  • ...In other words, GRNs are sparsely connected....

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  • ...Several different approaches such as Boolean networks [5], and S-systems [6] are used to construct GRNs....

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  • ...The methodology proposed in [10] remarkably fails to predict any true positives in the case of the fourth experiment whereas our proposed framework does not fail to infer true positives for any experiment....

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  • ...To circumvent this problem, researchers have proposed problem decomposition strategies wherein the global problem of optimising the entire set of parameters is split into several local sub-problems of optimising the parameters corresponding to a single target gene only [9-10]....

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  • ...genetic algorithm (GA) [7], differential evolution [8-9], particle swarm optimisation [9-10], etc....

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