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|a 10.1016/j.eswa.2021.116063
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|a eng
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| 100 |
1 |
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|a Issa, Mohamed
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism
|b Case study COVID-19
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 21.12.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 Elsevier Ltd. All rights reserved.
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|a The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta-heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods
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| 650 |
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|a Journal Article
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| 650 |
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|a BA algorithm
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| 650 |
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|a COVID-19
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| 650 |
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|a Computaional Biology
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| 650 |
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|a Longest common consecutive subsequence (LCCS)
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| 650 |
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|a Meta-heuristics
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| 700 |
1 |
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|a Helmi, Ahmed M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Elsheikh, Ammar H
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Abd Elaziz, Mohamed
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Expert systems with applications
|d 1999
|g 189(2022) vom: 01. März, Seite 116063
|w (DE-627)NLM098196782
|x 0957-4174
|7 nnas
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| 773 |
1 |
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|g volume:189
|g year:2022
|g day:01
|g month:03
|g pages:116063
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| 856 |
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|u http://dx.doi.org/10.1016/j.eswa.2021.116063
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