A novel biclustering approach with iterative optimization to analyze gene expression data.

ABSTRACT:

Objective

With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically.

Results

We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overl

SUBMITTER: Sutheeworapong S 

PROVIDER: S-EPMC3459542 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature