A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding

Xin Jin, Rencan Nie, Dongming Zhou, Shaowen Yao, Yanyan Chen, Jiefu Yu and Quan Wang

Physica A: Statistical Mechanics and its Applications, 2016, vol. 461, issue C, 325-338

Abstract: A novel method for the calculation of DNA sequence similarity is proposed based on simplified pulse-coupled neural network (S-PCNN) and Huffman coding. In this study, we propose a coding method based on Huffman coding, where the triplet code was used as a code bit to transform DNA sequence into numerical sequence. The proposed method uses the firing characters of S-PCNN neurons in DNA sequence to extract features. Besides, the proposed method can deal with different lengths of DNA sequences. First, according to the characteristics of S-PCNN and the DNA primary sequence, the latter is encoded using Huffman coding method, and then using the former, the oscillation time sequence (OTS) of the encoded DNA sequence is extracted. Simultaneously, relevant features are obtained, and finally the similarities or dissimilarities of the DNA sequences are determined by Euclidean distance. In order to verify the accuracy of this method, different data sets were used for testing. The experimental results show that the proposed method is effective.

Keywords: Simplified pulse-coupled neural network; DNA sequence; Sequence similarity; Huffman coding; Euclidean distance (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:461:y:2016:i:c:p:325-338

DOI: 10.1016/j.physa.2016.05.004

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