Reconstructing gene regulatory network with enhanced particle swarm optimization
This conference paper was presented in the 21st International Conference on Neural Information Processing, ICONIP 2014; Kuching; Malaysia; 3 November 2014 through 6 November 2014 [© Springer International Publishing Switzerland 2014]
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| 語言: | English |
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© 2014 Springer International Publishing Switzerland
2016
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| 在線閱讀: | http://hdl.handle.net/10361/7340 |
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10361-73402022-01-27T03:12:55Z Reconstructing gene regulatory network with enhanced particle swarm optimization Sultana, Rezwana Showkat, Dilruba Samiullah, Md. Chowdhury, Ahsan Raja Aja Department of Computer Science and Engineering, BRAC University Genetic network Linear time variant Microarray This conference paper was presented in the 21st International Conference on Neural Information Processing, ICONIP 2014; Kuching; Malaysia; 3 November 2014 through 6 November 2014 [© Springer International Publishing Switzerland 2014] Inferring regulations among the genes is a well-known and significantly important problem in systems biology for revealing the fundamental cellular processes. Although computational models can be used as tools to extract the probable structure and dynamics of such networks from gene expression data, capturing the complex nonlinear system dynamics is a challenging task. In this paper, we have proposed a method to reverse engineering Gene Regulatory Network (GRN) from microarray data. Inspired from the biologically relevant optimization algorithm ‘Particle Swarm Optimization’ (PSO), we have enhanced the PSO incorporating two genetic algorithm operators, namely crossover and mutation. Furthermore, Linear Time Variant (LTV) Model is employed to modeling the GRN appropriately. In the evaluation, the proposed method shows superiority over the state-of-the-art methods when tested with synthetic network, both for the noise free and noise in data. The strength of the proposed method has also been verified by analyzing the real expression data set of SOS DNA repair system in Escherichia coli. Published 2016-12-26T06:25:53Z 2016-12-26T06:25:53Z 2014 Conference paper Sultana, R., Showkat, D., Samiullah, M., & Chowdhury, A. R. (2014). Reconstructing gene regulatory network with enhanced particle swarm optimization 3029743 http://hdl.handle.net/10361/7340 en © 2014 Springer International Publishing Switzerland |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Genetic network Linear time variant Microarray |
| spellingShingle |
Genetic network Linear time variant Microarray Sultana, Rezwana Showkat, Dilruba Samiullah, Md. Chowdhury, Ahsan Raja Aja Reconstructing gene regulatory network with enhanced particle swarm optimization |
| description |
This conference paper was presented in the 21st International Conference on Neural Information Processing, ICONIP 2014; Kuching; Malaysia; 3 November 2014 through 6 November 2014 [© Springer International Publishing Switzerland 2014] |
| author2 |
Department of Computer Science and Engineering, BRAC University |
| author_facet |
Department of Computer Science and Engineering, BRAC University Sultana, Rezwana Showkat, Dilruba Samiullah, Md. Chowdhury, Ahsan Raja Aja |
| format |
Conference paper |
| author |
Sultana, Rezwana Showkat, Dilruba Samiullah, Md. Chowdhury, Ahsan Raja Aja |
| author_sort |
Sultana, Rezwana |
| title |
Reconstructing gene regulatory network with enhanced particle swarm optimization |
| title_short |
Reconstructing gene regulatory network with enhanced particle swarm optimization |
| title_full |
Reconstructing gene regulatory network with enhanced particle swarm optimization |
| title_fullStr |
Reconstructing gene regulatory network with enhanced particle swarm optimization |
| title_full_unstemmed |
Reconstructing gene regulatory network with enhanced particle swarm optimization |
| title_sort |
reconstructing gene regulatory network with enhanced particle swarm optimization |
| publisher |
© 2014 Springer International Publishing Switzerland |
| publishDate |
2016 |
| url |
http://hdl.handle.net/10361/7340 |
| work_keys_str_mv |
AT sultanarezwana reconstructinggeneregulatorynetworkwithenhancedparticleswarmoptimization AT showkatdilruba reconstructinggeneregulatorynetworkwithenhancedparticleswarmoptimization AT samiullahmd reconstructinggeneregulatorynetworkwithenhancedparticleswarmoptimization AT chowdhuryahsanrajaaja reconstructinggeneregulatorynetworkwithenhancedparticleswarmoptimization |
| _version_ |
1814309260723486720 |