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Mining Order-Preserving Submatrices Based on Frequent Sequential Pattern Mining

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 8423))

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Abstract

Order-Preserving Submatrices (OPSMs) have been widely accepted as a pattern-based biclustering and used in gene expression data analysis. The OPSM problem aims at finding the groups of genes that exhibit similar rises and falls under some certain conditions. However, most methods are heuristic algorithms which are unable to reveal PSOMs entirely. In this paper, we proposed an exact method to discover all OPSMs based on frequent sequential pattern mining. Firstly, an algorithm is adjusted to disclose all common subsequences (ACS) between every two sequences. Then an improved data structure for prefix tree was used to store and traverse all common subsequences, and Apriori Principle was employed to mine the frequent sequential pattern efficiently. Finally, the experiments were implemented on a real data set and GO analysis was applied to identify whether the patterns discovered were biological significant. The results demonstrate the effectiveness and the efficiency of this method.

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Author information

Authors and Affiliations

  1. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, China, 510006

    Yun Xue, Yuting Li, Weijun Deng, Jiejin Li, Jianxiong Tang, Zhengling Liao & Tiechen Li

Authors
  1. Yun Xue

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  2. Yuting Li

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  3. Weijun Deng

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  4. Jiejin Li

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  5. Jianxiong Tang

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  6. Zhengling Liao

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  7. Tiechen Li

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Editor information

Editors and Affiliations

  1. Centre for Applied Informatics, Victoria University, 8001, Melbourne, VIC, Australia

    Yanchun Zhang  & Xiaoxia Yin  & 

  2. Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, UK

    Guiqing Yao

  3. College of Engineering and Science, Victoria University, 8001, Melbourne, VIC, Australia

    Jing He

  4. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, 518055, Shenzhen, China

    Lei Wang

  5. Psychiatric Institute, University of Illinois at Chicago, MC912, 1601 W. Taylor Street, 60612, Chicago, IL, USA

    Neil R. Smalheiser

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© 2014 Springer International Publishing Switzerland

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Xue, Y.et al. (2014). Mining Order-Preserving Submatrices Based on Frequent Sequential Pattern Mining. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_20

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