一种基于邻域的组合中心性方法

A Combined Neighborhood-based Centrality Method

关键蛋白质是各种生命活动的重要参与者,在生命生存和繁殖中起着至关重要的作用.基于邻域的中心性方法是识别关键蛋白质的常用方法.提出基于邻域组合的中心性方法(CNC),将度中心性方法(DC)与局部平均连接中心性方法(LAC)进行组合,并引入参数α调节DC方法的影响权重.实验表明,与现有其它邻域中心性方法相比,CNC方法提高了静态和动态蛋白质相互作用网络的关键蛋白质识别率.

Essential protein is important participants in various life activities and plays a vital role in the survival and reproduction of life.The neighborhood-based centrality method is the important method to identify essential protein.A combined neighborhood-based centrality method was proposed,which combines degree centrality(DC)and local average connectivity(LAC).Meantime,the parameterαto adjust the weight of the DC was introduced.The experiment shows that compared with other neighborhood-based centrality methods,the CNC improves the essential protein recognition rate in static and dynamic proteinprotein interaction networks.