A hyper-heuristic for the Longest Common Subsequence problem

Tabataba, Farzaneh Sadat; Mousavi, Seyed Rasoul

doi:10.1016/j.compbiolchem.2011.12.004

Cited by 26 publications

(23 citation statements)

References 17 publications

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“…The algorithm can learn during the search process, whose procedure is better for a particular instance or at a particular time (more on hyper-heuristics can be found in [ 34 – 36 ]). Hyper-heuristics have already been successfully applied to some bioinformatics problems (e.g., [ 37 – 39 ]).…”

Section: Methodsmentioning

confidence: 99%

GRASShopPER—An algorithm for de novo assembly based on GPU alignments

et al. 2018

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Next generation sequencers produce billions of short DNA sequences in a massively parallel manner, which causes a great computational challenge in accurately reconstructing a genome sequence de novo using these short sequences. Here, we propose the GRASShopPER assembler, which follows an approach of overlap-layout-consensus. It uses an efficient GPU implementation for the sequence alignment during the graph construction stage and a greedy hyper-heuristic algorithm at the fork detection stage. A two-part fork detection method allows us to identify repeated fragments of a genome and to reconstruct them without misassemblies. The assemblies of data sets of bacteria Candidatus Microthrix, nematode Caenorhabditis elegans, and human chromosome 14 were evaluated with the golden standard tool QUAST. In comparison with other assemblers, GRASShopPER provided contigs that covered the largest part of the genomes and, at the same time, kept good values of other metrics, e.g., NG50 and misassembly rate.

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Section: Methodsmentioning

confidence: 99%

GRASShopPER—An algorithm for de novo assembly based on GPU alignments

et al. 2018

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“…The LCS algorithm is a similarity measure that is successfully used for sequence matching (Nyirarugira and Kim, 2015). The LCS algorithm aims to find a common subsequence with the longest possible length in two sequences (Chowdhury vd., 2014;Tabataba and Mousavi, 2012). As it is seen in Equation ( 3), the algorithm needs to be repeated until all characters are matched.…”

Section: Longest Common Subsequence Algorithmmentioning

confidence: 99%

Comparison of the Data Matching Performances of String Similarity Algorithms in Big Data

Aksoy

Uğuz

Oral

2019

Mühendislik Bilimleri Ve Tasarım Dergisi

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The great mobility in the world tourism in recent years has also enabled this sector to be included among the study areas of big data. In this study, a solution proposal was put forward by using the big data and string similarity algorithms (SSA) for the problems arising from the entry of the hotel data coming from different providers into databases with different names and addresses. Therefore, 2599 hotels of a tourism agency with a wide hotel network located in London were selected as the sample, and the Map-Reduce process was performed by using the Soundex algorithm to match these hotels with approximately three million hotel data coming from seventy different providers. Matching with Map-Reduce ensured a significant reduction in process count and process time. Furthermore, the Dice coefficient, Levenshtein and Longest common subsequence (LCS) algorithms were compared in terms of the data that they correctly matched, and process time. In this stage, the words decreasing the score of the algorithms in the database were detected and removed before the algorithms were implemented. The Dice coefficient algorithm yielded better results in terms of correct matching, and the Levenshtein algorithm yielded better results in terms of process time.

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“…For arbitrary number of sequences, the problem of computing an LCS is NP-hard [28]. As a result a number of heuristic and/or meta-heuristic techniques have also been applied in the literature to solve the LCS problem for arbitrary number of sequences (e.g., [7,31,34,35]). The related problem of global alignment, which is more relevant in computational biology, has also received much attention in the literature.…”

Section: Introductionmentioning

confidence: 99%