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http://dx.doi.org/10.3745/KIPSTD.2009.16-D.3.327

Macroscopic Biclustering of Gene Expression Data  

Ahn, Jae-Gyoon (연세대학교 컴퓨터과학과)
Yoon, Young-Mi (가천의과학대학교)
Park, Sang-Hyun (연세대학교 컴퓨터과학과)
Publication Information
The KIPS Transactions:PartD / v.16D, no.3, 2009 , pp. 327-338 More about this Journal
Abstract
A microarray dataset is 2-dimensional dataset with a set of genes and a set of conditions. A bicluster is a subset of genes that show similar behavior within a subset of conditions. Genes that show similar behavior can be considered to have same cellular functions. Thus, biclustering algorithm is a useful tool to uncover groups of genes involved in the same cellular process and groups of conditions which take place in this process. We are proposing a polynomial time algorithm to identify functionally highly correlated biclusters. Our algorithm identifies 1) the gene set that has hidden patterns even if the level of noise is high, 2) the multiple, possibly overlapped, and diverse gene sets, 3) gene sets whose functional association is strongly high, and 4) deterministic biclustering results. We validated the level of functional association of our method, and compared with current methods using GO.
Keywords
Data Mining; Biclustering; Gene Expression Data Analysis; Microarray Analysis; Noise;
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