Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm
13 Jun 2017-Journal of Bioinformatics and Computational Biology (World Scientific Publishing Company)-Vol. 15, Iss: 4, pp 1750016-1750016
TL;DR: A new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN) is proposed, mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques.
Abstract: Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.
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TL;DR: RMPSO is applied to a practical scenario: the reconstruction of Gene Regulatory Networks (GRN) based on Recurrent Neural Network (RNN) model and the experimental results ensure that the RMPSO performs better than the state-of-the-art methods in the synthetic gene data set (gold standard) as well as real gene data data set.
40 citations
TL;DR: Results show the efficiency of MESWSA algorithm for I-V characteristics of solar modules at different operating conditions can serve as a new alternative metaheuristic for parameter estimation of solar cells/PV modules.
Abstract: A highly accurate modeling of photovoltaic (PV) systems from experimental data is a very important task for electronic engineers for efficient design of PV systems. Suitable optimization techniques...
12 citations
01 Aug 2019
TL;DR: In this work, three different improved versions of original elephant swarm water search algorithm (ESWSA) is proposed and tested against the present problem of liquid flow control and ESWSA is found to be best efficient algorithm with respect to success rate and computational time.
Abstract: In process industry, liquid flow rate is one of the important variables which need to be controlled to obtain the better quality and reduce the cost of production. The liquid flow rate depends upon number of parameters like sensor output voltage, pipe diameter etc. Conventional approach involves manual tuning of these variables so that optimal flow rate can be achieved which is time consuming and costly. However, estimation of an accurate computational model for liquid flow control process can serve as alternative approach. It is nothing but a non-linear optimization problem. In this work, three different improved versions of original elephant swarm water search algorithm (ESWSA) is proposed and tested against the present problem of liquid flow control. Equations for response surface methodology and analysis of variance are being used as non-linear models and these models are optimized using those newly proposed optimization techniques. The statistical analysis of the obtained results shows that the proposed MESWSA has highest overall efficiency (i.e. 45%) and it outperformed the others techniques for the most of the cases of modeling for liquid flow control process. But one of the major disadvantages of MESWSA is its slow convergence speed. On the other hand, ESWSA is better for finding the best fitness and LESWSA has better stability in output. Moreover, LMESWSA is found to be best efficient algorithm with respect to success rate and computational time. However, all algorithms and models can predict the liquid flow rate with satisfactory accuracy.
7 citations
TL;DR: This work has proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system, which uses half the number of parameters compared to S-systems and thus significantly reduce the computational complexity.
Abstract: The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system . Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3 . The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases.
6 citations
Cites methods from "Recurrent neural network-based mode..."
...Recently, a new metaheuristic, namely, elephant swarm water search (ESWS) algorithm was proposed byMandal [58] for training the RNNmodel parameters [59]....
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TL;DR: In this article, the authors identify the proper combination of input parameters in TIG welding of martensitic stainless steel AISI 420 and identify a critical operating region in terms of maximum UTS and Ductility.
Abstract: Martensitic stainless steels are hard, brittle and notch sensitive; crack formation during welding is frequent. Selection of the levels of welding parameters i.e. the input variables seems to be important and useful in the context of achieving optimum/maximum strength of the welded joint. In the present work, focus is given on identification of the proper combination of input parameters in TIG welding of martensitic stainless steel AISI 420. Welding current, gas flow rate and welding speed have been taken as input parameters. Ultimate tensile strength (UTS) and Ductility or Elongation of the welded joint obtained from tensile test is taken as response parameter. Initially, response surface methodology based face-centered central composite design has been used for mathematical model building and regression analysis. Next, several recently proposed metaheuristics are applied for parametric optimization of TIG welding process to maximize the response parameters. From, the simulated results, a critical operating region for efficient TIG welding is identified in term of maximum UTS and Ductility. Confirmatory tests are also performed to validate our proposed methodology.
5 citations
References
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01 Jan 2013
TL;DR: This work used the decoupled version of the recurrent neural network (RNN) model for gene network inference from gene expression data and applied a well established evolutionary algorithm called differential evolution for inferring the underlying network structure as well as the regulatory parameters.
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.
46 citations
TL;DR: The availability in computerized form of the published literature on genes is a potentially rich source of information for the interpretation of microarray data and can reveal functional information that is useful in explaining gene expression patterns.
Abstract: The availability in computerized form of the published literature on genes is a potentially rich source of information for the interpretation of microarray data. Automated text processing confronts substantial challenges due to variability in the language used by authors, but even incomplete linking of gene clusters to the literature can reveal functional information that is useful in explaining gene expression patterns.
37 citations
TL;DR: A Genetic Algorithm-Recurrent Neural Network (GA-RNN) hybrid method for finding feed-forward regulated genes when given some transcription factors to construct cancer-related regulatory modules in human cancer microarray data and correctly identifies known oncogenes and their interaction genes in a purely data-driven way.
Abstract: Background
Modeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new diagnose and therapies. Several mathematical models have been used to explore the phenomena of transcriptional regulatory mechanisms in Saccharomyces cerevisiae. However, the contemplating on controlling of feed-forward and feedback loops in transcriptional regulatory mechanisms is not resolved adequately in Saccharomyces cerevisiae, nor is in human cancer cells.
35 citations
TL;DR: A pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems and shows that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method.
Abstract: Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method (SPEM) and a pruning strategy, which includes adding an \ell_1 regularization term to the objective function and pruning the solution with a threshold value. Then, this algorithm is combined with the continuous genetic algorithm (CGA) to form a hybrid algorithm that owns the properties of these two combined algorithms. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The results show that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method.
35 citations