Seed-based biclustering of gene expression data.

ABSTRACT:

Background

Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner.

Methods

In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditio

SUBMITTER: An J 

PROVIDER: S-EPMC3411756 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature