Title: Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering

Authors: Levent Carkacioglu, Rengul Cetin Atalay, Ozlen Konu, Volkan Atalay, Tolga Can

Addresses: Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. ' Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey. ' Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey. ' Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. ' Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

Abstract: Due to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations.

Keywords: biclustering; APD; association pattern discovery; Spearman rank correlation; gene expression analysis; data mining; bioinformatics; association rules; gene pairs.

DOI: 10.1504/IJDMB.2010.037548

International Journal of Data Mining and Bioinformatics, 2010 Vol.4 No.6, pp.701 - 721

Received: 05 Jul 2008
Accepted: 05 Jan 2009
Published online: 16 Dec 2010 *

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