RESEARCH ARTICLE


Fuzzy String Matching Procedure



Zekâi Şen1, 2, *
1 Engineering and Natural Sciences Faculty, Istanbul Medipol University, Beykoz 34181, Istanbul, Turkey
2 Department of Meteorology, Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia


© 2020 Zekâi Şen.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Engineering and Natural Sciences Faculty, Istanbul Medipol University, Beykoz 34181, Istanbul, Turkey; Tel: 0532 342 6043; E-mail: zsen@medipol.edu.tr


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.

Keywords: DNA, Fuzzy- logic, Match, Membership degree, String, Knuth-Morris-Pratt algorithms.