DiCluster approach: effective mining differential co-expression bicluster in gene expression data
从基因表达数据中有效挖掘差异共表达双聚类：DiCluster算法

The conception of bicluster is proposed by using the approach of mining on gene set and condition set parallelly. It can find genes which are co-expression under some conditions. Traditional algorithms find biclusters from only one dataset, while it is biologically meaningful to mine among a couple of datasets. This paper proposed the Dicluster algorithm. It extended nodes with the strategy of depth-prior and added several pruning steps to mine maximal differential co-expression biclusters effectively. The result of experiment shows DiCluster is more efficient than current algorithms. And the result is more statistically and biologically significant.