Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion

摘要: In a functional analysis of gene expression data, biclustering method can give crucial information by showing correlated patterns under subset conditions. However, conventional algorithms still have some limitations to show comprehensive and stable outputs. We propose novel approach called “BIclustering Correlated Large number Individual Clustered seeds (BICLIC)” find sets in biclusters using clustered their expansion with correlation expression. BICLIC outperformed competing completely recovering implanted simulated datasets various types patterns: shifting, scaling, shifting-scaling. Furthermore, real yeast microarray dataset lung cancer dataset, found more that are significantly enriched diverse biological terms than those other algorithms. provides significant benefits finding implications from dataset.