Biclustering methods: biological relevance and application in gene expression analysis.

ABSTRACT: DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or me

SUBMITTER: Oghabian A 

PROVIDER: S-EPMC3961251 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature