Discovery of error-tolerant biclusters from noisy gene expression data.

ABSTRACT:

Background

An important analysis performed on microarray gene-expression data is to discover biclusters, which denote groups of genes that are coherently expressed for a subset of conditions. Various biclustering algorithms have been proposed to find different types of biclusters from these real-valued gene-expression data sets. However, these algorithms suffer from several limitations such as inability to explicitly handle errors/noise in the data; difficulty in discovering small bicliusters due to their top-down approach; inability of some of the approaches to find overlapping biclusters, which is crucial as many genes participate in multiple biological processes. Association pattern mining also produce biclusters as their result and can naturally address some of these limitation

SUBMITTER: Gupta R 

PROVIDER: S-EPMC3247082 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature