A visual analytics approach for understanding biclustering results from microarray data.

ABSTRACT:

Background

Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results.

Results

We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by

SUBMITTER: Santamaria R 

PROVIDER: S-EPMC2416653 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature