摘要
为了解决目前的关键蛋白质预测方法对生物功能的分析不够深入的情况,利用蛋白质复合物信息,提出1种基于随机游走模型,结合蛋白质相互作用网络中的边聚集系数等数据来预测关键蛋白质的RWP(random walk method for predicting essential proteins)算法。在酿酒酵母(Saccharomyces cerevisiae)蛋白质相互作用网络上,以敏感度、特异性、阳性预测值、阴性预测值、准确率等5个统计学指标为评价标准,将RWP与介数中心性、度中心性、信息中心性、CSC算法及LIDC算法等5种用于预测关键蛋白质的方法进行对比实验。结果表明:RWP在关键蛋白质识别率等方面优于这5种测度方法,它具有较好的预测关键蛋白质的性能。
The method for predicting essential proteins based on protein-protein interaction network is not deep enough to discover biological functions.In order to solve this problem,we utilize the protein complex information and propose an algorithm named RWP based on random walk model combining with the edge clustering coefficients in PPI network to recognize essential proteins.In protein-protein interaction network of Saccharomyces cerevisiae , 5 criteria of statistics evaluation criteria such as SN etc were taken to experiment with RWP and five centrality measure methods （DC, etc.） contrastively.The results showed that the number of essential proteins predicted by RWP was more than that predicted by other five centrality measure methods.
作者
杨莉萍
路松峰
黄钰
YANG Li ping LU Songfeng HUANG Yu(College of Informatics , Huazhong Agricultural University ,Wuhan 430070 , China College of Computer Science and Technology , Huazhong University of Science and Technology ,Wuhan 430074 , China)
出处
《华中农业大学学报》
CAS
CSCD
北大核心
2016年第6期86-91,共6页
Journal of Huazhong Agricultural University
基金
国家自然科学基金项目(61173050)
中央高校基本科研业务费专项(2662015QC040)
关键词
关键蛋白质
随机游走模型
蛋白质互作网络
蛋白质复合物
边聚集系数
essential protein
random walk model
protein-protein interaction network
protein complex
edge clustering coefficient
