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dc.contributor.authorŞen, Zekai
dc.date.accessioned2020-12-22T11:56:45Z
dc.date.available2020-12-22T11:56:45Z
dc.date.issued2020en_US
dc.identifier.citationŞen, Z. (2020). Fuzzy string matching procedure. Open Bioinformatics Journal, 13(1), 50-56. https://dx.doi.org/10.2174/1875036202013010050en_US
dc.identifier.issn1875-0362
dc.identifier.urihttps://dx.doi.org/10.2174/1875036202013010050
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6134
dc.description.abstractBackground: There are different methodologies for DNA comparison based on two string algorithms, which are dependent on crisp logical principles, where there is no room for verbal (linguistic) uncertainty. These are successfully applicable procedures in DNA bioinformatics researches even by taking into consideration probabilistic random variability components based on the probability distribution functions of various types. Objective: The main purpose of this paper is to review first briefly all available DNA string matching methodologies that are based on crisp logic and then to suggest a new method based on the fuzzy logic rules and application. Methods: There are different methodologies for DNA comparison based on two string algorithms, which are dependent on crisp logical principles, where there is no room for verbal (linguistic) uncertainty. These are successfully applicable procedures in DNA bioinformatics researchers even by taking into consideration probabilistic random variability components based on the probability distribution functions of various types. Results: Fuzzy number representation of each gene implies some sort of uncertainty or unhealthiness in some or all the genes. Their better identifications can be achieved on the basis of fuzzy numbers with different membership degrees, which imply the unhealthiness or healthiness of the genes and their collective behaviors. Conclusion: After the development of fuzzy number representation of the text string coupled with crisp pattern string their relationships are searched at different shift operations, and hence, the possibility of defaulters are identified in the text string with a certain degree of membership.en_US
dc.language.isoengen_US
dc.publisherBentham Science Publishersen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcode*
dc.subjectDNAen_US
dc.subjectFuzzy- Logicen_US
dc.subjectMatchen_US
dc.subjectMembership Degreeen_US
dc.subjectStringen_US
dc.subjectKnuth-Morris-Pratt Algorithmsen_US
dc.titleFuzzy string matching procedureen_US
dc.typearticleen_US
dc.relation.journalOpen Bioinformatics Journalen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.authorid0000-0003-2754-5492en_US
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.startpage50en_US
dc.identifier.endpage56en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.2174/1875036202013010050en_US


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