Measuring the Score Matching of the Pairwise Deoxyribonucleic Acid Sequencing using Neuro-Fuzzy

Keywords: Component, Dynamic programming, Matching, Neuro-fuzzy, Sequence alignment

Abstract

The proposed model for getting the score matching of the deoxyribonucleic acid (DNA) sequence is introduced; the Neuro-Fuzzy procedure is the strategy actualized in this paper; it is used the collection of biological information of the DNA sequence performing with global and local calculations so as to advance the ideal arrangement; we utilize the pairwise DNA sequence alignment to gauge the score of the likeness, which depend on information gathering from the pairwise DNA series to be embedded into the implicit framework; an adaptive neuro-fuzzy inference system model is reasonable for foreseeing the matching score through the preparation and testing in neural system and the induction fuzzy system in fuzzy logic that accomplishes the outcome in elite execution.

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References

R. Bhukya and D. V. L. Somayajulu. “Exact multiple pattern matching algorithm using DNA sequence and pattern pair”. International Journal of Computer Applications, vol. 17, no. 8, pp. 32-38, 2011.

X. Xie, J. Guan and S. Zhou. “Similarity evaluation of DNA sequences based on frequent patterns and entropy”. BMC Genomics, vol. 16, no. 3, p. S5, 2015.

W. Deng and Y. Luan. “Analysis of similarity/dissimilarity of DNA sequences based on chaos game representation”. Abstract and Applied Analysis, vol. 2013, p. 926519, 2013.

P. Pandiselvam, T. Marimuthu and R. Lawrance. “A Comparative Study on String Matching Algorithms of Biological Sequences”. In: International Conference on Intelligent Computing, pp. 1-5, 2014.

T. Chakrabarti, S. Saha and D. Sinha. “DNA multiple sequence alignment by a hidden markov model and fuzzy levenshtein distance based genetic algorithm”. International Journal of Computer Applications, vol. 73, no. 16, pp. 26-30, 2013.

N. Gill and S. Singh. “Biological sequence matching using fuzzy logic”. International Journal of Scientific and Engineering Research, vol. 2, no. 7, pp. 1-5, 2011.

S. B. Needleman and C. D. Wunsch. “A general method applicable to the search for similarities in the amino acid sequence of two proteins”. Journal of Molecular Biology, vol. 48, no. 3, pp. 443-453, 1970.

T. F. Smith and M. S. Waterman. “Identification of common molecular subsequences”. Journal of Molecular Biology, vol. 147, no. 1, pp. 195-197, 1981.

D. Nauck, F. Klawonn and R. Kruse. “Foundations of Neuro-Fuzzy Systems”. John Wiley and Sons, Inc., New York, 1997.

S. Nasser, G. L. Vert, M. Nicolescu and A. Murray. “Multiple Sequence Alignment Using Fuzzy Logic. In: 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, IEEE, pp. 304-311, 2007.

K. Kim, M. Kim and Y. Woo. “A DNA sequence alignment algorithm using quality information and a fuzzy inference method”. Progress in Natural Science, vol. 18, no. 5, pp. 595-602, 2008.

N. Chai, L. R. Swem, M. Reichelt, H. Chen-Harris, E. Luis, S. Park and J. McBride. “Two escape mechanisms of influenza a virus to a broadly neutralizing stalk-binding antibody”. PLoS Pathogens, vol. 12, no. 6, p. e1005702, 2016.

S. A. Hameed and R. I. Hamed. “Analysing the score matching of dna sequencing using an expert system of neurofuzzy”. Journal of Theoretical and Applied Information Technology, vol. 95, no. 6, pp. 1255-1262, 2017.

DNA Matching Data Base-NCBI”. https://www.ncbi.nlm.nih.gov/nucleotide.

Published
2019-08-20
How to Cite
Hameed, S., & Hamed, R. (2019). Measuring the Score Matching of the Pairwise Deoxyribonucleic Acid Sequencing using Neuro-Fuzzy. Cihan University-Erbil Scientific Journal, 3(2), 37-41. https://doi.org/10.24086/cuesj.v3n2y2019.pp37-41
Section
Research Article