Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
Author
Çarkacioǧlu, L.
Atalay, R.
Konu, O.
Atalay, V.
Can, T.
Date
2010Source Title
International Journal of Data Mining and Bioinformatics
Print ISSN
1748-5673
Publisher
Inderscience Enterprises Ltd.
Volume
4
Issue
6
Pages
701 - 721
Language
English
Type
ArticleItem Usage Stats
109
views
views
101
downloads
downloads
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. Copyright © 2010 Inderscience Enterprises Ltd.
Keywords
APDAssociation pattern discovery
Biclustering
Gene expression analysis
Spearman rank correlation