计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (32): 132-133.DOI: 10.3778/j.issn.1002-8331.2008.32.039

• 数据库、信号与信息处理 • 上一篇    下一篇

基于蛋白质相互作用网络图的聚类方法

彭利红,廖 波,刘 昊   

  1. 湖南大学 计算机与通信学院,长沙 410082
  • 收稿日期:2007-12-13 修回日期:2008-03-07 出版日期:2008-11-11 发布日期:2008-11-11
  • 通讯作者: 彭利红

Clustering method based on protein-protein interaction network graph

PENG Li-hong,LIAO Bo,LIU Hao   

  1. School of Computer and Communication,Hunan University,Changsha 410082,China
  • Received:2007-12-13 Revised:2008-03-07 Online:2008-11-11 Published:2008-11-11
  • Contact: PENG Li-hong

摘要: 依据人类AD(Alzheimer’s Disease)相关蛋白质相互作用网络图,利用基于算术平均最小值——AAMV(Arithmetic Average Minimum Value)的K-means聚类方法对蛋白质进行聚类并预测4个孤立蛋白质的功能。分析结果表明:所得结果与用Maryland Bridge 法及Korbel法所得结果非常相似。

关键词: 蛋白质相互作用网络图, 算术平均最小值, K-means聚类方法, 准则函数

Abstract: The purpose of this study is to evaluate a novel clustering technique for clustering and detecting the isolated points in protein-protein interaction network,which iteratively refines clusters based on a combination of the K-means clustering algorithm and the Arithmetic Average Minimum Value(AAMV).The result is that the algorithm is found to be effective at detecting clusters and identifying the isolated points in the protein-protein interaction network graph with regard to human Alzheimer’s Disease.The algorithm outperforms competing approaches and is capable of effectively predicting the function-unknown protein function.

Key words: protein-protein interaction network graph, Arithmetic Average Minimum Value(AAMV), K-means clustering method, criterion function