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New Construction of Family of MLCS Algorithms

The multiple longest common subsequence (MLCS) problem involves finding all the longest common subsequences of multiple character sequences. This problem is encountered in a variety of areas, including data mining, text processing, and bioinformatics, and is particularly important for biological seq...

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Detalles Bibliográficos
Autores principales: Shi, Haihe, Wang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843170/
https://www.ncbi.nlm.nih.gov/pubmed/33542799
http://dx.doi.org/10.1155/2021/6636710
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author Shi, Haihe
Wang, Jun
author_facet Shi, Haihe
Wang, Jun
author_sort Shi, Haihe
collection PubMed
description The multiple longest common subsequence (MLCS) problem involves finding all the longest common subsequences of multiple character sequences. This problem is encountered in a variety of areas, including data mining, text processing, and bioinformatics, and is particularly important for biological sequence analysis. By taking the MLCS problem and algorithms for its solution as research domain, this study analyzes the domain of multiple longest common subsequence algorithms, extracts features that algorithms in the domain do and do not have in common, and creates a domain feature model for the MLCS problem by using generic programming, domain engineering, abstraction, and related technologies. A component library for the domain is designed based on the feature model for the MLCS problem, and the partition and recur (PAR) platform is used to ensure that highly reliable MLCS algorithms can be quickly assembled through component assembly. This study provides a valuable reference for obtaining rapid solutions to problems of biological sequence analysis and improves the reliability and assembly flexibility of assembling algorithms.
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spelling pubmed-78431702021-02-03 New Construction of Family of MLCS Algorithms Shi, Haihe Wang, Jun J Healthc Eng Research Article The multiple longest common subsequence (MLCS) problem involves finding all the longest common subsequences of multiple character sequences. This problem is encountered in a variety of areas, including data mining, text processing, and bioinformatics, and is particularly important for biological sequence analysis. By taking the MLCS problem and algorithms for its solution as research domain, this study analyzes the domain of multiple longest common subsequence algorithms, extracts features that algorithms in the domain do and do not have in common, and creates a domain feature model for the MLCS problem by using generic programming, domain engineering, abstraction, and related technologies. A component library for the domain is designed based on the feature model for the MLCS problem, and the partition and recur (PAR) platform is used to ensure that highly reliable MLCS algorithms can be quickly assembled through component assembly. This study provides a valuable reference for obtaining rapid solutions to problems of biological sequence analysis and improves the reliability and assembly flexibility of assembling algorithms. Hindawi 2021-01-19 /pmc/articles/PMC7843170/ /pubmed/33542799 http://dx.doi.org/10.1155/2021/6636710 Text en Copyright © 2021 Haihe Shi and Jun Wang. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Shi, Haihe
Wang, Jun
New Construction of Family of MLCS Algorithms
title New Construction of Family of MLCS Algorithms
title_full New Construction of Family of MLCS Algorithms
title_fullStr New Construction of Family of MLCS Algorithms
title_full_unstemmed New Construction of Family of MLCS Algorithms
title_short New Construction of Family of MLCS Algorithms
title_sort new construction of family of mlcs algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843170/
https://www.ncbi.nlm.nih.gov/pubmed/33542799
http://dx.doi.org/10.1155/2021/6636710
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