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Application of Classification Association Rule Mining for Mammalian Mesenchymal Stem Cell Differentiation

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5633))

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Abstract

In this paper, data mining is used to analyze the differentiation of mammalian Mesenchymal Stem Cells (MSCs). A database comprising the key parameters which, we believe, influence the destiny of mammalian MSCs has been constructed. This paper introduces Classification Association Rule Mining (CARM) as a data mining technique in the domain of tissue engineering and initiates a new promising research field. The experimental results show that the proposed approach performs well with respect to the accuracy of (classification) prediction. Moreover, it was found that some rules mined from the constructed MSC database are meaningful and useful.

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Authors and Affiliations

  1. Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, UK

    Weiqi Wang, René Bañares-Alcántara & Zhanfeng Cui

  2. Information Management Center, China Minsheng Banking Corp., Ltd., Room 606, Building No. 8, 1 Zhongguancun Nandajie, Beijing, 100873, China

    Yanbo J. Wang

  3. Department of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool, L69 3BX, UK

    Frans Coenen

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  1. Weiqi Wang
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  2. Yanbo J. Wang
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  3. René Bañares-Alcántara
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  4. Zhanfeng Cui
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  5. Frans Coenen
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Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland

    Petra Perner

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Wang, W., Wang, Y.J., Bañares-Alcántara, R., Cui, Z., Coenen, F. (2009). Application of Classification Association Rule Mining for Mammalian Mesenchymal Stem Cell Differentiation. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-03067-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03066-6

  • Online ISBN: 978-3-642-03067-3

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