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International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 208–218Cite as

Fast Significant Matches of Position Weight Matrices Based on Diamond Sampling

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

Abstract

Position weight matrices are important method for modeling signals or motifs in biological sequences, both in DNA and protein contexts. In this paper, we present techniques for increasing the speed of sequence analysis using position weight matrices. Our techniques also permit the user to specify a p threshold to indicate the desired trade-off between sensitivity and speed for a particular sequence analysis. The resulting increase in speed should allow our algorithm to be used more widely in searching with large-scale sequence and annotation projects.

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Acknowledgments

This work was supported by the grants of the National Science Foundation of China, Nos. 61672203, 61402334, 61472282, 61520106006, 31571364, U1611265, 61472280, 61532008, 61472173, 61572447, 61373098 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646. De-Shuang Huang is the corresponding author of this paper.

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Authors and Affiliations

  1. Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China

    Liang-xin Gao, Hong-bo Zhang & Lin Zhu

Authors
  1. Liang-xin Gao
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  2. Hong-bo Zhang
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  3. Lin Zhu
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Correspondence to Liang-xin Gao .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Gao, Lx., Zhang, Hb., Zhu, L. (2017). Fast Significant Matches of Position Weight Matrices Based on Diamond Sampling. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

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