Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification.

ABSTRACT:

Background

Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.

Results

Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseas

SUBMITTER: Gormley M 

PROVIDER: S-EPMC2620272 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature