%0 Journal Article
%T Mining maximal dense subgraphs in uncertain PPI network<br>基于不确定性PPI网络的最大稠密子图挖掘*
%A LIU Jia-cai
%A SHANG Xue-qun
%A MENG Ya
%A WANG Miao
%A <br>刘加财
%A 尚学群
%A 孟雅
%A 王淼
%J 计算机应用研究
%D 2011
%I 
%X Several studies have shown that the prediction of protein function using PPI data is promising. However, the PPI data generated from experiments are noisy, incomplete and inaccurate, which promotes to represent PPI dataset as an uncertain graph. This paper proposed a novel algorithm to mine maximal dense subgraphs efficiently in uncertain PPI network. It adopted several techniques to achieve efficient mining. An extensive experimental evaluation on yeast PPI network demonstrates that the approach has good performance in terms of precision and efficiency.
%K PPI network
%K uncertain graph
%K dense subgraph
%K expected density<br>PPI网络
%K 不确定图
%K 稠密子图
%K 期望支持度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1F8EB868F38CE07259B3D20A2C166ED0&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=708DD6B15D2464E8&sid=D96D4F2692C092DE&eid=ECD93AFA1AA9E157&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10