Measuring similarities between gene expression profiles through new data transformations.

ABSTRACT:

Background

Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is critical to the analysis. In our study, we developed a new measure for clustering the genes when the key factor is the shape of the profile, and when the expression magnitude should also be accounted for in determining the gene relationship. This is achieved by modeling the shape and magnitude parameters separately in a gene expression profile, and then using the estimated shape and magnitude parameters to define a measure in a new feature space.

Results

We explored several different transformation schemes to construct the feature spaces that include a space whose features are determined by the mutual di

SUBMITTER: Kim K 

PROVIDER: S-EPMC1804284 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature