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

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Date

2010-01-01

Author

Carkacioglu, Levent
Atalay, Rengül
KONU KARAKAYALI, ÖZLEN
Atalay, Mehmet Volkan
Can, Tolga

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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.

Subject Keywords

Biclustering, APD, Association pattern discovery, Spearman rank correlation, Gene expression analysis

URI

https://hdl.handle.net/11511/32311

Journal

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS

DOI

https://doi.org/10.1504/ijdmb.2010.037548

Collections

Graduate School of Informatics, Article

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Citation Formats

L. Carkacioglu, R. Atalay, Ö. KONU KARAKAYALI, M. V. Atalay, and T. Can, “Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering,” INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, pp. 701–721, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32311.