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
More filters
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]....
[...]
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
More filters
TL;DR: Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems.
Abstract: Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.
582 citations
TL;DR: A novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GNW, which provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods.
Abstract: Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Results: Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). Availability: GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary information: Supplementary data are available at Bioinformatics online.
539 citations
TL;DR: A successful MSR elephant study is reported and striking parallels in the progression of responses to mirrors among apes, dolphins, and elephants are reported to suggest convergent cognitive evolution most likely related to complex sociality and cooperation.
Abstract: Considered an indicator of self-awareness, mirror self-recognition (MSR) has long seemed limited to humans and apes. In both phylogeny and human ontogeny, MSR is thought to correlate with higher forms of empathy and altruistic behavior. Apart from humans and apes, dolphins and elephants are also known for such capacities. After the recent discovery of MSR in dolphins (Tursiops truncatus), elephants thus were the next logical candidate species. We exposed three Asian elephants (Elephas maximus) to a large mirror to investigate their responses. Animals that possess MSR typically progress through four stages of behavior when facing a mirror: (i) social responses, (ii) physical inspection (e.g., looking behind the mirror), (iii) repetitive mirror-testing behavior, and (iv) realization of seeing themselves. Visible marks and invisible sham-marks were applied to the elephants' heads to test whether they would pass the litmus "mark test" for MSR in which an individual spontaneously uses a mirror to touch an otherwise imperceptible mark on its own body. Here, we report a successful MSR elephant study and report striking parallels in the progression of responses to mirrors among apes, dolphins, and elephants. These parallels suggest convergent cognitive evolution most likely related to complex sociality and cooperation.
507 citations
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
TL;DR: A comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate, and the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
454 citations