一种新的用于加权网络的复合物挖掘算法  

Novel algorithm for mining protein complex from weighted network

作　　者：汤希玮[1] 胡伟[1] 胡秋玲[1] 

机构地区：[1]湖南第一师范学院信息科学与工程系,长沙410205

出　　处：《计算机应用研究》2014年第7期2017-2020,共4页Application Research of Computers

基　　金：湖南省自然科学基金面上项目(13JJ6086);湖南省软科学研究计划重点资助项目(2013ZK2014)

摘　　要：大部分生物过程由蛋白质复合物实现。蛋白质相互作用数据集中大量存在的假阳性降低了数据集的可用性。为了减轻假阳性带来的不良影响,许多数据整合与亲缘打分方案被设计。这些方案通过给蛋白质网络中的边(蛋白质之间的相互作用)指定权值,从而提高了蛋白质网络的可信度。目前的挑战是如何从加权的蛋白质网络中挖掘新的有生物学意义的蛋白质复合物。为了解决这一问题,提出了一种新的蛋白质复合物挖掘算法BFSWD(breadth first search with weighted density)。该算法以加权密度为条件,从单个蛋白质(种子)出发,广度优先搜索加权蛋白质网络,进而构建蛋白质复合物。实验结果表明,在蛋白质复合物挖掘方面,BFSWD算法优于其他类似的方法。Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction（PPI） data can compromise the utility of the data sets for complexes reconstruction. In order to reduce the impact of such discrepancies,a number of data integration and affinity scoring schemes have been devised. The methods enhance reliabilities（confidence） of the protein network by defining a weighted value between each pair of proteins. The challenge now is to mine novel and meaningful protein complexes from the weighted PPI network. To address this problem,this paper proposed a novel protein complex mining algorithm（ BFSWD,breadth first search with weighted density）. Based on the weighted density,BFSWD mines protein complexes of the weighted network by the breadth first search algorithm,which originates from a given‘seed＇protein used as starting-point. The experimental results show that BFSWD performs significantly better than the other computational approaches in terms of the identification of protein complexes.

关 键 词：广度优先搜索 加权蛋白质相互作用网络 蛋白质复合物 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]