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BMCBI
2006

Effect of data normalization on fuzzy clustering of DNA microarray data

8 years 6 months ago
Effect of data normalization on fuzzy clustering of DNA microarray data
Background: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. Results: In this study we applied the fuzzy partitional clustering method known as Fuzzy C-Means (FCM) to overcome the limitations of hard clustering. To identify the effect of data normalization, we used three normalization methods, the two common scale and location transformations and Lowess normalization methods, to normalize three microarray datasets and ...
Seo Young Kim, Jae Won Lee, Jong Sung Bae
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where BMCBI
Authors Seo Young Kim, Jae Won Lee, Jong Sung Bae
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