Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs

Authors

  • Patryk Orzechowski AGH University of Science and Technology, Kraków
  • Krzysztof Boryczko AGH University of Science and Technology, Kraków

Keywords:

Biclustering, bioinformatics, pattern matching, data mining, microarray gene expression data, conserved gene expression motifs

Abstract

Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions. Biclustering algorithms may be also applied to different datasets, such as medical, economical, social networks etc. In this article we explain the concept beneath hybrid biclustering algorithms and present details of propagation-based biclustering, a novel approach for extracting inclusion-maximal gene expression motifs conserved in gene microarray data. We prove that this approach may successfully compete with other well-recognized biclustering algorithms.

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Author Biographies

Patryk Orzechowski, AGH University of Science and Technology, Kraków

Department of Automatics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, researching-and-teaching assistant

Krzysztof Boryczko, AGH University of Science and Technology, Kraków

Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science

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Published

2016-07-11

How to Cite

Orzechowski, P., & Boryczko, K. (2016). Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs. COMPUTING AND INFORMATICS, 35(2), 391–410. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/1804