%0 Journal Article
%T DiCluster approach: effective mining differential co-expression bicluster in gene expression data<br>从基因表达数据中有效挖掘差异共表达双聚类：DiCluster算法
%A LI Xiao-yuan
%A SHANG Xue-qun
%A WANG Miao
%A <br>李晓园
%A 尚学群
%A 王　淼
%J 计算机应用研究
%D 2012
%I 
%X 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.
%K gene expression data
%K bicluster
%K differential co-expression<br>基因表达数据
%K 双聚类
%K 差异共表达
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=3337477751B1F60253659C1EF321C73D&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=708DD6B15D2464E8&sid=EB0CA356C0937F93&eid=8910CB39AD1C556D&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=16