Quantum Computing Based Inference of GRNs
26 Apr 2017-pp 221-233
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.
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Dissertation•
01 Jan 2018
TL;DR: This dissertation aims to provide a chronology of the events leading up to and including the invention of the determinants of infectious disease.
Abstract: .................................................................................................................. III List of Figures ......................................................................................................... VI List of Tables ....................................................................................................... VIII Attestation of Authorship ........................................................................................ X Acknowledgement .................................................................................................. XI
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TL;DR: In this article , an efficient and scalable vulnerability detection method based on a deep neural network model, Long Short-Term Memory (LSTM), and quantum machine learning model (QLSTM) is presented.
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TL;DR: Zhang et al. as mentioned in this paper proposed a particle swarm optimization (PSO) based approach to infer genetic regulatory networks from time series gene expression data, which can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.
Abstract: Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.
173 citations
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
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
01 Dec 2015
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.
1 citations