摘要
不断增长的蛋白质相互作用数据使我们能够采用计算方法预测蛋白质复合物。然而,由于实验条件和技术的限制,现有的PPI网络中包含噪声。为了降低噪声对复合物识别所产生的负面影响,提出了一种改进的名为WPC的算法,用于从加权网络中识别蛋白质复合物。给定一个选定节点,所有邻居节点组成候选集,候选集中节点的邻居节点组成邻居集。对于候选集中的节点,若该节点在候选集与邻居集间的加权比低于设定阈值,则将该点剔除。处理后的候选集被标记为复合物。对于没有包含在任何复合物中的节点,如果节点在某一复合物内的平均加权度超过一个自适应的阈值,则将其补充到该复合物中。对WPC算法和现有的几种经典蛋白质复合物识别算法的性能进行了综合比较。实验结果表明,WPC算法的性能优于几种对比的复合物识别算法。
The increasing amount of protein-protein interaction （PPI） data has enabled us to predict protein complexes.Due to the limitation of experimental conditions and techniques,there is a lot of noise in the PPI networks.To reduce the negative effects of noise on protein complex prediction,a new improved method named WPC （Weighted-network based method for Predicting protein Complexes） was proposed.Given a selected node,candidate set consists of all neighbors of the node and neighbor set consists of neighbors of all nodes in the candidate set.If the weighted ratio of a node between the candidate set and the neighbor set is lower than a threshold,the node is removed from the candidate set.After repeating the process for all nodes in the candidate set,the candidate set is represented as a complex.For a node not being included in any complexes,if its average weighted degree within a complex exceeds a self-adjustment threshold,WPC adds the node to the complex.A comprehensive comparison among the competitive algorithms and WPC was made.Experimental results show that WPC outperforms the state-of-the-art methods.
出处
《计算机科学》
CSCD
北大核心
2014年第6期231-234,共4页
Computer Science
基金
国家自然科学基金(61232001)
湖南省十二五规划课题(XJK011CXJ002)资助
关键词
平均加权度
蛋白质复合物
蛋白质相互作用网络
加权比
Average weighted degree
Protein complex
Protein-protein interaction network
Weighted ratio
