%0 Journal Article
%T Prediction of protein complexes based on weighted network<br>从加权网络中预测蛋白质复合物
%A TANG Xi-wei
%A LI Yong-fan
%A HU Qiu-ling
%A <br>汤希玮
%A 李勇帆
%A 胡秋玲
%J 计算机应用研究
%D 2012
%I 
%X The computational methods predicting protein complexes in the protein-protein interaction network have a great error because of the high false positive rate and false negative rate of protein interaction data. To compensate for this, this paper constructed a new weighted protein interaction network via the integration of the protein-protein interaction and gene expression data. To evaluate the biological significance of the network, it used MCL algorithm to detect protein complexes in the weighted network and unweighted one. Matching analysis was performed between the derived complexes and benchmark complexes. The results show that the weighted network outperforms the unweighted network on biology.
%K protein-protein interaction
%K gene expression profiles
%K protein complexes
%K matching statistics<br>蛋白质相互作用
%K 基因表达谱
%K 蛋白质复合物
%K 匹配统计
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=8D8174D12B4331BF9D7356D7AF051131&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=59906B3B2830C2C5&sid=02C5112DE36845CC&eid=C1BFEAF4AC03D615&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=21