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Protein Classification Using Random Walk on Graph

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Emerging Intelligent Computing Technology and Applications (ICIC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 304))

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Abstract

Inspired by the label propagation based on random walk, a basic classifier is proposed for biological computation. We apply this model to predict the localization of proteins, specifically the yeast and gram-negative bacteria protein data. A series of evaluations and comparisons are conducted to prove its excellent performance as an effectively independent classifier.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, Yangzhou University, Yangzhou, 225009, China

    Xiaohua Xu, Lin Lu, Ping He, Zhoujin Pan & Cheng Jing

Authors
  1. Xiaohua Xu
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  2. Lin Lu
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  3. Ping He
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  4. Zhoujin Pan
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  5. Cheng Jing
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Xu, X., Lu, L., He, P., Pan, Z., Jing, C. (2012). Protein Classification Using Random Walk on Graph. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_26

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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