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An Effective Biclustering Algorithm for Time-Series Gene Expression Data

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Book cover Machine Learning and Cybernetics (ICMLC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

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Abstract

The biclustering is a useful tool in analysis of massive gene expression data, which performs simultaneous clustering on rows and columns of the data matrix to find subsets of coherently expressed genes and conditions. Especially, in analysis of time-series gene expression data, it is meaningful to restrict biclusters to contiguous time points concerning coherent evolutions. In this paper, the BCCC-Bicluster is proposed as an extension of the CCC-Bicluster. An algorithm based on the frequent sequential mining is proposed to find all maximal BCCC-Biclusters. The newly defined Frequent-Infrequent Tree-Array (FITA) is constructed to speed up the traversal process, with useful strategies originating from Apriori Property to avoid redundant search. To make it more efficient, the bitwise operation XOR is applied to capture identical or opposite contiguous patterns between two rows. The algorithm is tested on the yeast microarray data. Experimental results show that the proposed algorithm is able to find all embedded BCCC-Biclusters, which are proven to reveal significant GO terms involved in biological processes.

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Authors and Affiliations

  1. School of Mathematical Sciences, South China Normal University, Guangdong, China

    Huixin Xu

  2. School of Physics and Telecommunication, South China Normal University, Guangdong, China

    Yun Xue, Xiaohui Hu, Zhengling Liao & Tiechen Li

  3. Computer School, South China Normal University, Guangdong, China

    Zhihao Lu

  4. Industrial Center, Shenzhen Polytechnic, Shenzhen, China

    Hongya Zhao

Authors
  1. Huixin Xu
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  2. Yun Xue
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  3. Zhihao Lu
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  4. Xiaohui Hu
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  5. Hongya Zhao
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  6. Zhengling Liao
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  7. Tiechen Li
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Corresponding author

Correspondence to Yun Xue .

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Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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Xu, H. et al. (2014). An Effective Biclustering Algorithm for Time-Series Gene Expression Data. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_12

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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