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Proceedings ArticleDOI

Genetic Regulatory Network Inference using Recurrent Neural Networks trained by a Multi Agent System

TL;DR: A novel algorithm for gene regulatory network inference that uses RNN with standard PSO for training and the results show improvements using the E. coli SOS dataset.
Abstract: We propose a novel algorithm for gene regulatory network inference. Gene Regulatory Network (GRN) inference is approximating the combined effect of different genes in a specific genome data. GRNs are nonlinear, dynamic and noisy. Timeseries data has been frequently used for GRN modeling. Due to the function approximation and feedback nature of GRN, a Recurrent Neural Network (RNN) model is used. RNN training is a complicated task. We propose a multi agent system for RNN training. The agents of the proposed multi agent system trainer are separate swarms of particles building up a multi population Particle Swarm Optimization (PSO) algorithm. We compare the proposed algorithm with a similar algorithm that uses RNN with standard PSO for training. The results show improvements using the E. coli SOS dataset. KeywordsGene Regulatory Network Inference, Particle Swarm Optimization, Multi Population PSO, Recurrent Neural Networks, Multi Agent Systems

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Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors present a survey of integration methods that reconstruct regulatory networks using state-of-the-art techniques to handle multi-omics (i.e., genomic, transcriptomic, proteomic) and other biological datasets.

40 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a recurrent neural network (RNN) based hybrid model of gene regulatory network (GRN), which is able to capture complex, non-linear and dynamic relationships among variables.

39 citations

Journal ArticleDOI
TL;DR: A recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm, and a comparison of the results with other state-of-the-art techniques shows superiority of the proposed model.
Abstract: Systems biology is an emerging interdisciplinary area of research that focuses on study of complex interactions in a biological system, such as gene regulatory networks. The discovery of gene regulatory networks leads to a wide range of applications, such as pathways related to a disease that can unveil in what way the disease acts and provide novel tentative drug targets. In addition, the development of biological models from discovered networks or pathways can help to predict the responses to disease and can be much useful for the novel drug development and treatments. The inference of regulatory networks from biological data is still in its infancy stage. This paper proposes a recurrent neural network (RNN) based gene regulatory network (GRN) model hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between the biological closeness and mathematical flexibility to model GRN. The RNN is able to capture complex, non-linear and dynamic relationship among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation even in noisy data. Hence, non-linear version of Kalman filter, i.e., generalized extended Kalman filter has been applied for weight update during network training. The developed model has been applied on DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We compared our results with other state-of-the-art techniques that show superiority of our model. Further, 5% Gaussian noise has been added in the dataset and result of the proposed model shows negligible effect of noise on the results.

39 citations


Cites background from "Genetic Regulatory Network Inferenc..."

  • ...Another improvement in RNN training algorithm has been proposed by Ghazikhani and his colleagues (Ghazikhani et al., 2011)....

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  • ...…maps (SOM) (Weaver et al., 1999) and recurrent neural networks (RNNs) (Vohradsky, 2001; Keedwell et al., 2002; Tian & Burrage, 2003; Xu et al., 2004; Hu et al., 2005; Chiang & Chao, 2007; Xu et al, 2007a; Xu et.al, 2007b; Ghazikhani et al., 2011; Noman et al., 2013; Raza et al., 2014)....

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  • ...…et al., 2004; Xu et al., 2004; Hu et al., 2005; Jung & Cho, 2007, Xu et al., 2007a; Xu et al., 2007b; Chiang & Chao, 2007; Lee & Yang, 2008; Datta et al., 2009; Zhang et al., 2009; Maraziotis et al., 2010; Ghazikhani et al., 2011; Liu et al., 2011; Kentzoglanakis, 2012; Noman et al., 2013), etc....

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  • ...Since, GRN is non-linear and feedback in nature; hence RNN model is quite suitable for modeling GRNs (Ghazikhani et al., 2011)....

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  • ..., 1999) and recurrent neural networks (RNNs) (Vohradsky, 2001; Keedwell et al., 2002; Tian & Burrage, 2003; Xu et al., 2004; Hu et al., 2005; Chiang & Chao, 2007; Xu et al, 2007a; Xu et.al, 2007b; Ghazikhani et al., 2011; Noman et al., 2013; Raza et al., 2014)....

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Book ChapterDOI
Khalid Raza1
TL;DR: Artificial intelligence based techniques for the analysis of microarray gene expression data are reviewed and challenges in the field and future work direction have also been suggested.
Abstract: Microarray is one of the essential technologies used by the biologist to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in more better way. Here, we reviewed artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.

25 citations


Additional excerpts

  • ...…et al., 2004; Xu et al., 2004; Hu et al., 2006; Jung and Cho, 2007; Xu et al., 2007a,b; Chiang and Chao, 2007; Lee and Yang, 2008; Datta et al., 2009; Zhang et al., 2009; Maraziotis et al., 2010; Ghazikhani et al., 2011; Liu et al., 2011; Kentzoglanakis and Poole, 2012; Noman et al., 2013), etc....

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Journal ArticleDOI
TL;DR: Given the intrinsic interdisciplinary nature of gene regulatory network inference, this work presents a review on the currently available approaches, their challenges and limitations and proposes guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.
Abstract: The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.

24 citations

References
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Journal ArticleDOI
01 Jan 1990
TL;DR: This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis, and describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method.
Abstract: Basic backpropagation, which is a simple method now being widely used in areas like pattern recognition and fault diagnosis, is reviewed. The basic equations for backpropagation through time, and applications to areas like pattern recognition involving dynamic systems, systems identification, and control are discussed. Further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations, or true recurrent networks, and other practical issues arising with the method are described. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed. The focus is on designing a simpler version of backpropagation which can be translated into computer code and applied directly by neutral network users. >

4,572 citations


"Genetic Regulatory Network Inferenc..." refers methods in this paper

  • ...Different approaches such as Back Propagation Through Time (BPTT) [12] and Evolutionary Algorithms (EAs) [13] have been used....

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Journal ArticleDOI
TL;DR: The development of each of these procedures over the past 35 years is described and some recent efforts in these areas are reviewed.
Abstract: Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed. >

1,549 citations


"Genetic Regulatory Network Inferenc..." refers methods in this paper

  • ...Different approaches such as Back Propagation Through Time (BPTT) [12] and Evolutionary Algorithms (EAs) [13] have been used....

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Journal ArticleDOI
TL;DR: The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data, and opening the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.
Abstract: A basic challenge in systems biology is to understand the dynamical behavior of gene regulation networks. Current approaches aim at determining the network structure based on genomic-scale data. However, the network connectivity alone is not sufficient to define its dynamics; one needs to also specify the kinetic parameters for the regulation reactions. Here, we ask whether effective kinetic parameters can be assigned to a transcriptional network based on expression data. We present a combined experimental and theoretical approach based on accurate high temporal-resolution measurement of promoter activities from living cells by using green fluorescent protein (GFP) reporter plasmids. We present algorithms that use these data to assign effective kinetic parameters within a mathematical model of the network. To demonstrate this, we employ a well defined network, the SOS DNA repair system of Escherichia coli. We find a strikingly detailed temporal program of expression that correlates with the functional role of the SOS genes and is driven by a hierarchy of effective kinetic parameter strengths for the various promoters. The calculated parameters can be used to determine the kinetics of all SOS genes given the expression profile of just one representative, allowing a significant reduction in complexity. The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data. This finding opens the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.

539 citations


"Genetic Regulatory Network Inferenc..." refers background in this paper

  • ...Genes initials are in lower cases, proteins in capital letters[21] ICCKE2011, International Conference on Computer and Knowledge Engineering Oct 13-14, 2011, Ferdowsi University of Mashhad, Mashhad, Iran...

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  • ...coli SOS DNA repair network dataset [21] (Fig....

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Journal ArticleDOI
TL;DR: A phenomenological modeling framework for development based on a connectionist or "neural net" dynamics for biochemical regulators coupled to "grammatical rules" which describe certain features of the birth, growth, and death of cells, synapses and other biological entities is presented.

478 citations


"Genetic Regulatory Network Inferenc..." refers methods in this paper

  • ...[2,3] used differential equations to model GRNs and used Recurrent Neural Networks (RNN) to learn the GRN....

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Journal ArticleDOI
27 Sep 2003
TL;DR: This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach that can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement.
Abstract: This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.

462 citations


"Genetic Regulatory Network Inferenc..." refers methods in this paper

  • ...To finalize the results we have used the genetic regulatory network regulations referenced in [22]....

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