Partition decoupling for multi-gene analysis of gene expression profiling data.

ABSTRACT:

Background

Multi-gene interactions likely play an important role in the development of complex phenotypes, and relationships between interacting genes pose a challenging statistical problem in microarray analysis, since the genes involved in these interactions may not exhibit marginal differential expression. As a result, it is necessary to develop tools that can identify sets of interacting genes that discriminate phenotypes without requiring that the classification boundary between phenotypes be convex.

Results

We describe an extension and application of a new unsupervised statistical learning technique, known as the Partition Decoupling Method (PDM), to gene expression microarray data. This method may be used to classify samples based on multi-gene expression patterns and

SUBMITTER: Braun R 

PROVIDER: S-EPMC3276603 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature