基于结构洞的多数据源融合关键蛋白质识别方法

doi:10.11896/jsjkx.200200004

Abstract

Abstract: Essential protein identification is a hot research topic which is difficult in the field of computational biology.The exis-ting methods for identifying essential proteins by computational methods are mainly DC,BC,LAC,PeC,ION,and LIDC,yet the identification accuracy needs to be further improved,mainly because only one data source is used which is protein interaction network,and there are many false positive and false negative data in the network.In order to improve the identification accuracy,an efficient essential protein identification method PSHC is proposed.Firstly,the PSHC method introduced the structure hole theory into the essential protein identification method for the first time.Secondly,the PSHC method combines two data sources of protein interaction network and protein complex to identify the essential proteins.Experimental results on real data show that PSHC can identify more essential proteins than other traditional methods,and statistical indicators such as sensitivity,specificity,accuracy,positive predictive value,negative predictive value,and F-measure are also higher than other methods.

Key words: Essential proteins, Protein complex, Protein interaction network, Structural holes

CLC Number:

TP301

YANG Zhuang, LIU Pei-qiang, FEI Zhao-jie, LIU Chang. Essential Protein Identification Method Based on Structural Holes and Fusion of Multiple Data Sources[J].Computer Science, 2020, 47(11A): 40-45.

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