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Journal Article•DOI•

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.
Citations
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Journal Article•DOI•
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

Journal Article•DOI•
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

Journal Article•DOI•
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

Journal Article•DOI•
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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Journal Article•DOI•
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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Journal Article•DOI•
19 May 2016
TL;DR: The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.
Abstract: We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

31 citations

Journal Article•DOI•
TL;DR: The Immune Algorithm (IA), a heuristic search method inspired by the biological mechanism of acquired immunity, was applied to search for the S-system parameters and showed higher performance than GA for both simulation and real data analyses.
Abstract: The S-system model is one of the nonlinear differential equation models of gene regulatory networks, and it can describe various dynamics of the relationships among genes. If we successfully infer rigorous S-system model parameters that describe a target gene regulatory network, we can simulate gene expressions mathematically. However, the problem of finding an optimal S-system model parameter is too complex to be solved analytically. Thus, some heuristic search methods that offer approximate solutions are needed for reducing the computational time. In previous studies, several heuristic search methods such as Genetic Algorithms (GAs) have been applied to the parameter search of the S-system model. However, they have not achieved enough estimation accuracy. One of the conceivable reasons is that the mechanisms to escape local optima. We applied an Immune Algorithm (IA) to search for the S-system parameters. IA is also a heuristic search method, which is inspired by the biological mechanism of acquired immunity. Compared to GA, IA is able to search large solution space, thereby avoiding local optima, and have multiple candidates of the solutions. These features work well for searching the S-system model. Actually, our algorithm showed higher performance than GA for both simulation and real data analyses.

22 citations

Journal Article•DOI•
TL;DR: Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters and significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods are shown.
Abstract: The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

16 citations

Journal Article•DOI•
TL;DR: The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree and the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
Abstract: The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

12 citations

Journal Article•DOI•
TL;DR: This method allows retrieval of significant new information and testable hypotheses from a pool of deposited cancer gene expression experiments that are otherwise not apparent or appear insignificant from single measurements.
Abstract: Mutations in cancer-causing genes induce changes in gene expression programs critical for malignant cell transformation. Publicly available gene expression profiles produced by modulating the expression of distinct cancer genes may therefore represent a rich resource for the identification of gene signatures common to seemingly unrelated cancer genes. We combined automatic retrieval with manual validation to obtain a data set of high-quality gene microarray profiles. This data set was used to create logical models of the signaling events underlying the observed expression changes produced by various cancer genes and allowed to uncover unknown and verifiable interactions. Data clustering revealed novel sets of gene expression profiles commonly regulated by distinct cancer genes. Our method allows retrieval of significant new information and testable hypotheses from a pool of deposited cancer gene expression experiments that are otherwise not apparent or appear insignificant from single measurements. The complete results are available through a web-application at http://biodata.ethz.ch/cgi-bin/geologic.

10 citations