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SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining

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Data Mining for Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1807))

Abstract

Biclustering extracts coexpressed genes under certain experimental conditions, providing more precise insight into the genetic behaviors than one-dimensional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed a web service named SiBIC, which, using maximal frequent itemset mining, exhaustively discovers significant biclusters, which turn into networks of overlapping biclusters, where nodes are gene sets and edges show their overlaps in the detected biclusters. SiBIC provides a graphical user interface for manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/sibic/faces/index.jsp.

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Acknowledgements

Part of this research has been supported by MEXT KAKENHI #16H02868 and #17H01783, ACCEL (JPMJAC1503) and PRESTO of JST, FiDiPro of Tekes and AIPSE programme, Academy of Finland.

Author information

Authors and Affiliations

  1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan

    Kei-ichiro Takahashi & Sohiya Yotsukura

  2. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Japan

    David A. duVerle

  3. Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, Japan

    Ichigaku Takigawa

  4. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan

    Hiroshi Mamitsuka

  5. Department of Computer Science, Aalto University, Espoo, Finland

    Hiroshi Mamitsuka

Authors
  1. Kei-ichiro Takahashi
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  2. David A. duVerle
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  3. Sohiya Yotsukura
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  4. Ichigaku Takigawa
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  5. Hiroshi Mamitsuka
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Corresponding author

Correspondence to Kei-ichiro Takahashi .

Editor information

Editors and Affiliations

  1. Bioinformatics Center, Kyoto University, Uji, Kyoto, Japan

    Hiroshi Mamitsuka

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Takahashi, Ki., duVerle, D.A., Yotsukura, S., Takigawa, I., Mamitsuka, H. (2018). SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_8

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  • DOI: https://doi.org/10.1007/978-1-4939-8561-6_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8560-9

  • Online ISBN: 978-1-4939-8561-6

  • eBook Packages: Springer Protocols

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