摘要
聚类是蛋白质结构预测中重要的后处理步骤,许多结构预测中都采取了不同的聚类算法.而AP聚类算法通过在数据点之间传递消息,经过若干次迭代后达到一种稳定状态,是构思巧妙的聚类算法.文中把AP聚类算法应用于蛋白质结构预测中,并在7个不同的数据集上进行了实验.结果表明,在采用RMSD进行结构相似性度量的情况下,AP算法有67%的结果优于Rosetta聚类算法或相当,是一种适合蛋白质结构聚类的算法.
Clustering decoys is a necessary post-processing step for protein structures prediction. There are many clustering algorithms used in protein structures prediction. The Affinity Propagation （ AP ） clustering algorithm is ingenious as it propagate some information among all the data points,and reach a steady state after some iterations. This paper applies the affinity propagation clustering algorithm in protein structure prediction,and test it in seven different data sets. And then,we compare the exemplars with their native conformations with the RMSD metric. Results show that the Affinity Propagation clustering algorithm is better than Rosetta clustering algotirhm under 67%,and it is fit for protein structure prediction.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第11期154-157,共4页
Microelectronics & Computer
基金
国家自然科学基金项目(60970055)
关键词
蛋白质结构预测
AP聚类算法
RMSD
protein structure prediction
Affinity Propagation clustering algorithm
RMSD
