AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

ANN Based Features Selection Approach Using Hybrid GA-PSO for siRNA Design


Affiliations
1 Department of Computer Science, Assam University Silchar, Assam, India
2 Department of Agril. Biotechnology, Assam Agricultural University, Assam, India
 

siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.

Keywords

SIRNA, PSO, GA-PSO, Features Selection, ANN, Cost Evaluation, GA-BPNN, Heuristic Optimization.
User
Notifications
Font Size

  • McManus, M.T., and Sharp, P.A.: ‘Gene silencing in mammals by small interfering RNAs’, Nat Rev Genet, 2002, 3

  • Hannon, G.J., and Rossi, J.J.: ‘Unlocking the potential of the human genome with RNA interference’, Nature, 2004, 431

  • Lu, Z.J., and Mathews, D.H.: ‘OligoWalk: an online siRNA design tool utilizing hybridization thermodynamics’, Nucleic Acids Res, 2008, 36, (Web Server issue), pp. W104-108

  • Saetrom, P., and Snove, O.: ‘A comparison of siRNA efficacy predictors’, Biochem Biophys Res Commun, 2004, 321

  • Basiri, M.E., and Nemati, S.: ‘A novel hybrid ACO-GA algorithm for text feature selection’, in Editor (Ed.)^(Eds.): ‘Book A novel hybrid ACO-GA algorithm for text feature selection’ (2009, edn.), pp. 2561-2568

  • Aghdam, M.H., Ghasem-Aghaee, N., and Basiri, M.E.: ‘Text feature selection using ant colony optimization’, Expert Systems with Applications, 2009, 36, (3, Part 2), pp. 6843-6853

  • Yang, J., and Honavar, V.: ‘Feature subset selection using a genetic algorithm’, IEEE Intelligent Systems and their Applications, 1998, 13, (2), pp. 44-49

  • Zhao, X., Li, D., Yang, B., Ma, C., Zhu, Y., and Chen, H.: ‘Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton’, Applied Soft Computing, 2014, 24, pp. 585-596

  • Liu, Y., Qin, Z., Xu, Z., and He, X.: ‘Feature Selection with Particle Swarms’, in Zhang, J., He, J.H., and Fu, Y. (Eds.): ‘Computational and Information Science: First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004. Proceedings’ (Springer Berlin Heidelberg, 2005), pp. 425-430

  • Mojtaba Ahmadieh, K., Mohammad, T., and Mahdi Aliyari, S.: ‘A novel binary particle swarm optimization’, in Editor (Ed.)^(Eds.): ‘Book A novel binary particle swarm optimization’ (2007, edn.), pp. 1-6

  • Prasad, Y., Biswas, K.K., and Jain, C.K.: ‘SVM Classifier Based Feature Selection Using GA, ACO and PSO for siRNA Design’, in Tan, Y., Shi, Y., and Tan, K.C. (Eds.): ‘Advances in Swarm Intelligence: First International Conference, ICSI 2010, Beijing, China, June 12-15, 2010, Proceedings, Part II’ (Springer Berlin Heidelberg, 2010), pp. 307-314

  • Jain, C.K., and Prasad, Y.: ‘Feature selection for siRNA efficacy prediction using natural computation’, in Editor (Ed.)^(Eds.): ‘Book Feature selection for siRNA efficacy prediction using natural computation’ (2009, edn.), pp. 1759-1764

  • Sarmah, R., Begum, S. A. and Modi, M.K.: ‘A hybrid GA-ANN approach in building efficient model for prediction of siRNA knockdown efficiency in plant pathogens’, International Journal of Computer Science and Information Security, 2016, 14, (12), pp. 15

  • Sánchez-Maroño, N., Alonso-Betanzos, A., and Tombilla-Sanromán, M.: ‘Filter Methods for Feature Selection – A Comparative Study’, in Yin, H., Tino, P., Corchado, E., Byrne, W., and Yao, X. (Eds.): ‘Intelligent Data Engineering and Automated Learning - IDEAL 2007: 8th International Conference, Birmingham, UK, December 16-19, 2007. Proceedings’ (Springer Berlin Heidelberg, 2007), pp. 178-187

  • Liu, H., and Zhao, Z.: ‘Manipulating Data and Dimension Reduction Methods: Feature Selection’, in Meyers, R.A. (Ed.): ‘Computational Complexity: Theory, Techniques, and Applications’ (Springer New York, 2012), pp. 1790-1800

  • Liu, Q., Xu, Q., Zheng, V.W., Xue, H., Cao, Z., and Yang, Q.: ‘Multi-task learning for crossplatform siRNA efficacy prediction: an in-silico study’, BMC Bioinformatics, 2010, 11

  • Xue, B., Zhang, M., Browne, W.N., and Yao, X.: ‘A Survey on Evolutionary Computation Approaches to Feature Selection’, IEEE Transactions on Evolutionary Computation, 2016, 20, (4), pp. 606-626

  • A Kachitvichyanukul, V.: ‘Recent Advances in Adaptive Particle Swarm Optimization Algorithms’, in Editor (Ed.)^(Eds.): ‘Book Recent Advances in Adaptive Particle Swarm Optimization Algorithms’ (2008, edn.), pp.

  • Arumugam, M.S., and Rao, M.V.C.: ‘On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and ischolar_main mean square (RMS) variants for computing optimal control of a class of hybrid systems’, Applied Soft Computing, 2008, 8, (1), pp. 324-336

  • Chen, W.N., Zhang, J., Chung, H.S.H., Zhong, W.L., Wu, W.G., and Shi, Y.h.: ‘A Novel SetBased Particle Swarm Optimization Method for Discrete Optimization Problems’, IEEE Transactions on Evolutionary Computation, 2010, 14, (2), pp. 278-300

  • http://www.swarmintelligence.org/tutorials.php

  • Kennedy, J., and Eberhart, R.: ‘Particle Swarm Optimization’, 1995

  • Li, L., and Zhang, Y.: ‘An Improved Genetic Algorithm for the Traveling Salesman Problem’, in Huang, D.-S., Heutte, L., and Loog, M. (Eds.): ‘Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings’ (Springer Berlin Heidelberg, 2007), pp. 208-216

  • Liao, Y.-F., Yau, D.-H., and Chen, C.-L.: ‘Evolutionary algorithm to traveling salesman problems’, Computers & Mathematics with Applications, 2012, 64, (5), pp. 788-797

  • Shelokar, P.S., Siarry, P., Jayaraman, V.K., and Kulkarni, B.D.: ‘Particle swarm and ant colony algorithms hybridized for improved continuous optimization’, Applied Mathematics and Computation, 2007, 188, (1), pp. 129-142

  • Kao, Y.-T., and Zahara, E.: ‘A hybrid genetic algorithm and particle swarm optimization for multimodal functions’, Applied Soft Computing, 2008, 8, (2), pp. 849-857

  • Goldberg, D.E.: ‘Genetic Algorithms in Search, Optimization and Machine Learning’ (Addison-Wesley Longman Publishing Co., Inc., 1989. 1989)

  • Man, K.F., TANG, K.S., and Kwong, S.: ‘Genetic Algorithms: Concepts and Designs’ (Springer London, 2001. 2001)

  • Voratas, K.: ‘Comparison of Three Evolutionary Algorithms: GA, PSO, and DE’, Industrial Engineering & Management Systems, 2012, 11, (3), pp. 215-223

  • Hook, J.V., Sahin, F., and Arnavut, Z.: ‘Application of Particle Swarm Optimization for Traveling Salesman Problem to lossless compression of color palette images’, in Editor (Ed.)^(Eds.): ‘Book Application of Particle Swarm Optimization for Traveling Salesman Problem to lossless compression of color palette images’ (2008, edn.), pp. 1-5

  • Zhang, Y., Gong, D.w., and Cheng, J.: ‘Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification’, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14, (1), pp. 64-75

  • Turney, P.D.: ‘Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm’, Journal of Artificial Intelligence Research, 1995, 2, pp. 41

  • Min, F., Hu, Q., and Zhu, W.: ‘Feature selection with test cost constraint’, International Journal of Approximate Reasoning, 2014, 55, (1, Part 2), pp. 167-179

  • Haralick, R.M., Shanmugam, K., and Dinstein, I.: ‘Textural Features for Image Classification’, IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3, (6), pp. 610-621

  • Abraham, S., Sanyal, S., and Sanglikar, M.: ‘Particle swarm optimisation based Diophantine equation solver’, Int. J. Bio-Inspired Comput., 2010, 2, (2), pp. 100-114.


Abstract Views: 240

PDF Views: 146




  • ANN Based Features Selection Approach Using Hybrid GA-PSO for siRNA Design

Abstract Views: 240  |  PDF Views: 146

Authors

Ranjan Sarmah
Department of Computer Science, Assam University Silchar, Assam, India
Mahendra K. Modi
Department of Agril. Biotechnology, Assam Agricultural University, Assam, India
Shahin Ara Begum
Department of Computer Science, Assam University Silchar, Assam, India

Abstract


siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.

Keywords


SIRNA, PSO, GA-PSO, Features Selection, ANN, Cost Evaluation, GA-BPNN, Heuristic Optimization.

References