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New Construction of Family of MLCS Algorithms
Author(s) -
Haihe Shi,
Jun Wang
Publication year - 2021
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1155/2021/6636710
Subject(s) - computer science , subsequence , domain (mathematical analysis) , algorithm , longest common subsequence problem , abstraction , component (thermodynamics) , reliability (semiconductor) , feature (linguistics) , data mining , mathematics , mathematical analysis , philosophy , power (physics) , physics , linguistics , epistemology , quantum mechanics , bounded function , thermodynamics
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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