Fuzzy string matching procedure
| dc.authorid | 0000-0003-2754-5492 | |
| dc.contributor.author | Şen, Zekai | |
| dc.date.accessioned | 2020-12-22T11:56:45Z | |
| dc.date.available | 2020-12-22T11:56:45Z | |
| dc.date.issued | 2020 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümü | |
| dc.description.abstract | Background: 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. | |
| dc.identifier.citation | Şen, Z. (2020). Fuzzy string matching procedure. Open Bioinformatics Journal, 13(1), 50-56. https://dx.doi.org/10.2174/1875036202013010050 | |
| dc.identifier.doi | 10.2174/1875036202013010050 | |
| dc.identifier.endpage | 56 | |
| dc.identifier.issn | 1875-0362 | |
| dc.identifier.issue | 1 | |
| dc.identifier.scopusquality | Q3 | |
| dc.identifier.startpage | 50 | |
| dc.identifier.uri | https://dx.doi.org/10.2174/1875036202013010050 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/6134 | |
| dc.identifier.volume | 13 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Bentham Science Publishers | |
| dc.relation.ispartof | Open Bioinformatics Journal | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/legalcode | * |
| dc.subject | DNA | |
| dc.subject | Fuzzy- Logic | |
| dc.subject | Match | |
| dc.subject | Membership Degree | |
| dc.subject | String | |
| dc.subject | Knuth-Morris-Pratt Algorithms | |
| dc.title | Fuzzy string matching procedure | |
| dc.type | Article |
