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SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
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- Takahashi, Kei-ichiro
- Bioinformatics Center, Institute for Chemical Research, Kyoto University
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- duVerle, David A.
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo
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- Yotsukura, Sohiya
- Bioinformatics Center, Institute for Chemical Research, Kyoto University
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- Takigawa, Ichigaku
- Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University
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- Mamitsuka, Hiroshi
- Bioinformatics Center, Institute for Chemical Research, Kyoto University･Department of Computer Science, Aalto University
Description
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.
Journal
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- Methods in Molecular Biology
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Methods in Molecular Biology 1807 95-111, 2018-07-21
Springer New York
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Keywords
Details 詳細情報について
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- CRID
- 1050845764156743552
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- NII Article ID
- 120006552927
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- ISSN
- 10643745
- 19406029
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- HANDLE
- 2433/236183
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- PubMed
- 30030806
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- IRDB
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
