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Title: SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining
Authors: Takahashi, Kei-ichiro
duVerle, David A.
Yotsukura, Sohiya
Takigawa, Ichigaku
Mamitsuka, Hiroshi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-6607-5617 (unconfirmed)
Author's alias: 馬見塚, 拓
Keywords: Gene expression
Biclustering
Frequent itemset mining
Gene set network
Gene enrichment analysis
Issue Date: 21-Jul-2018
Publisher: Springer New York
Journal title: Methods in Molecular Biology
Volume: 1807
Start page: 95
End page: 111
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.
Rights: This is a post-peer-review, pre-copyedit version of an article published in Methods in Molecular Biology. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-1-4939-8561-6_8.
The full-text file will be made open to the public on 21 July 2019 in accordance with publisher's 'Terms and Conditions for Self-Archiving'
URI: http://hdl.handle.net/2433/236183
DOI(Published Version): 10.1007/978-1-4939-8561-6_8
PubMed ID: 30030806
Appears in Collections:Journal Articles

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