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A self-adaptive intelligent single-particle optimizer compression algorithm

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Abstract

This paper presents a self-adaptive intelligent single-particle optimizer (AdpISPO) for DNA sequence data compression codebook design. Featured with the crucial self-adaptive optimization process, AdpISPO is capable of attaining better fitness value than most existing particle swarm optimization variants with no specific parameters required. A novel DNA sequence data compression algorithm, namely BioSqueezer, is proposed in this paper. Introducing all the unique data features in constructing the compression codebook, BioSqueezer compresses DNA sequences by replacing similar fragments with the index of its corresponding code vector. For attaining higher compression ratio, the AdpISPO is employed in BioSqueezer for the codebook design. Experimental results on benchmark DNA sequences demonstrate that BioSqueezer attains better performance than other state-of-the-art DNA compression algorithms.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61103174, 61301182), Scientific and Technological Innovation Project of Department of Education of Guangdong Province (2013KJCX0162, 20134408120004), Natural Science Foundation of Guangdong Province (China) (S2013040016857, S2013010012227), Fundamental Research General Program of Shenzhen City (JCYJ20120613113535357, JCYJ20130329105415965), Research Fund of Shenzhen University Lab and Equipment Management.

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  1. College of Information Engineering, Shenzhen University, Shenzhen, 518060, Guangdong, People’s Republic of China

    Jie Zeng

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  1. Jie Zeng
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Correspondence to Jie Zeng.

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Zeng, J. A self-adaptive intelligent single-particle optimizer compression algorithm. Neural Comput & Applic 25, 1285–1292 (2014). https://doi.org/10.1007/s00521-014-1609-x

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  • DOI: https://doi.org/10.1007/s00521-014-1609-x

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