A swarm intelligence based scheme for reduction of false positives in inferred gene regulatory networks
24 Jul 2016-pp 40-47
TL;DR: This work has proposed a novel scheme, based on different swarm intelligence algorithms, to reduce the number of inferred false regulations in gene regulatory networks, and the obtained results suggest that the proposed methodology can reduce theNumber of false predictions, significantly, without using any supplementary biological information for larger gene Regulatory networks.
Abstract: A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This has somewhat overshadowed the reduction of false predictions in inferred networks. In the present investigation, we have proposed a novel scheme, based on different swarm intelligence algorithms, to reduce the number of inferred false regulations. We have first applied our proposed methodology on the much studied, benchmark experimental datasets of the DNA SOS repair network of Escherichia Coli. Subsequently, we have experimented upon a larger, in silico network extracted from the GeneNetWeaver database. The obtained results suggest that the proposed methodology can reduce the number of false predictions, significantly, without using any supplementary biological information for larger gene regulatory networks.
Citations
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TL;DR: A simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network, showing that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.
Abstract: Gene expressions are controlled by a series of processes known as Gene Regulation, and their abstract mapping is represented by Gene Regulatory Network (GRN) which is a descriptive model of gene interactions. Reverse engineering GRNs can reveal the complexity of gene interactions whose comprehension can lead to several other details. RNA-seq data provides better measurement of gene expressions, however it is difficult to infer GRNs using it because of its discreteness. Multiple other methods have already been proposed to infer GRN using RNA-seq data, but these methodologies are difficult to grasp. In this paper, a simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network. The obtained results show that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.
Cites background or methods from "A swarm intelligence based scheme f..."
...The functional circuitry of all living organisms is formed by genes [1]and synergistic actions between inter related genes is the reason of all biological reactions inside a cell....
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...Based on FA, PSO, BA-PSO which are swarm intelligence techniques, RNN formalism is used to investigate reverse engineering of GRNs from time series microarray datasets [1]....
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References
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TL;DR: The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations.
144 citations
"A swarm intelligence based scheme f..." refers methods in this paper
...V. CONCLUSION In this work, we have investigated the reverse engineering of GRNs from time series microarray datasets, based on the RNN formalism with three different swarm intelligence techniques like PSO, BA-PSO, and FA....
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...PRELIMINARIES AND BACKGROUND A. Recurrent Neural Network (RNN) The underlying dynamics of temporal genetic expression data can be accurately captured by the RNN formalism [6] as shown in Fig....
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...The objective of the RNN formalism, implemented here is to reproduce the given temporal genetic expression profiles accurately by accurately training the model parameters....
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...Mathematically, the RNN formalism adopted for modelling GRNs is as given below [6]:...
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...Each layer of the RNN describes the genetic expression level of the genes at a specified time ....
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01 Jan 2000
TL;DR: A linear model is fit to a data set on development and injury in the central nervous system, and a more realistic, nonlinear model is presented, resulting in a set of nonlinear differential equations equivalent to a specific type of recurrent neural network.
Abstract: New technologies have been developed to measure the expression level of thousands of genes simultaneously. These genomic-scale snapshots of gene expression—i.e. how much each gene is “turned on”—are creating a revolution in biology. However, such large-scale data also creates an urgent, need for computational tools to make sense of it all.
Genes encode proteins, some of which in turn regulate other genes. Now that the human genome is within our grasp, we need to start thinking of the next step: determining the structure of this intricate network of genetic regulatory interactions. Many different modeling methodologies could be used to model such gene networks. Analysis of various network models shows that, given a sufficiently constrained model, data requirements should scale well. Additive regulation models—where the regulatory inputs are combined using a weighted sum—can be used as a first-order approximation to the gene network. We can infer regulatory interactions directly from the data, by fitting these simple network models to large scale gene expression data. The amount of data typically is insufficient to derive a fully determined network model. Nevertheless, we can extract the most well-determined interactions in the network, using knowledge of the error levels on the measurements in a Monte-Carlo analysis of the resulting variability in the network parameters.
Using this methodology, a linear model is fit to a data set on development and injury in the central nervous system. The results compare favorably with the literature on the genes involved. Next, a more realistic, nonlinear model is presented, resulting in a set of nonlinear differential equations equivalent to a specific type of recurrent neural network. This model should allow for a closer fit with the biological reality, but requires more computational effort to fit to real data sets.
67 citations
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
"A swarm intelligence based scheme f..." refers methods or result in this paper
...The specificity and accuracy of the proposed model are equivalent or better in most of the cases, compared to the one in [10]....
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...Researchers have employed a decoupling scheme [10], where the reverse engineering problem has been basically divided into two problems: (i) search for a suitable, biologically plausible GRN, and (ii) proper training of the corresponding RNN model parameters....
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...The authors in [10] have also investigated into the four datasets separately, and hence, we have compared our results with their result....
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