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Pairwise Matching of Spots in 2-DE Images Using Hopfield Network

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Modeling Decisions for Artificial Intelligence (MDAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3558))

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Abstract

Matching spots between two-dimensional electrophoresis (2-DE) images is a bottleneck in the automation of proteome analysis. Because the matching problem is an NP-hard problem, the solution is usually a heuristic approach or a neural network method. So a Hopfield neural network approach is applied to solve this problem. An energy function is designed to represent the similarity of spots together with its neighbor spots. Experiment showed that Hopfield neural network with appropriate energy function and dynamics could solve the matching problem of spots in 2-DE images.

This work was supported by grant No. R01-2003-000-10860-0 from the Basic Research Program of the Korea Science & Engineering Foundation. This work was supported by the Sun Moon University Research Grant of 2002.

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Author information

Authors and Affiliations

  1. Division of Computer & Information, Sun Moon University, 336-708, Asan, Korea

    Young-Sup Hwang & Hoon Park

  2. Dept. of Computer Engineering, Hankuk Univ. of Foreign Studies, 89 Wangsan Mohyun, Yongin Kyounggi-do, 449-791, Korea

    Yoojin Chung

Authors
  1. Young-Sup Hwang
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  2. Hoon Park
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  3. Yoojin Chung
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Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

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© 2005 Springer-Verlag Berlin Heidelberg

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Hwang, YS., Park, H., Chung, Y. (2005). Pairwise Matching of Spots in 2-DE Images Using Hopfield Network. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_26

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  • DOI: https://doi.org/10.1007/11526018_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27871-9

  • Online ISBN: 978-3-540-31883-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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