PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-1 (1)
 

Clipboard (0)
None

Select a Filter Below

Journals
Authors
Year of Publication
Document Types
1.  A novel biclustering approach with iterative optimization to analyze gene expression data 
Video abstract
Video
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 overlap state to be explicitly considered. In addition, the average of the Pearson’s correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented.
Conclusion
BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms.
doi:10.2147/AABC.S32622
PMCID: PMC3459542  PMID: 23055751
biclustering; microarray data; genetic algorithm; Pearson’s correlation coefficient

Results 1-1 (1)