【摘 要】
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Essential proteins are regarded as the crucial components of organisms,and thus identifying essential proteins is a hot and significant topic in biomedical research.A great deal of computational metho
【出 处】
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第二届中国计算机学会生物信息学会议
论文部分内容阅读
Essential proteins are regarded as the crucial components of organisms,and thus identifying essential proteins is a hot and significant topic in biomedical research.A great deal of computational methods based on network topology have been proposed to characterize protein essentiality.However,the prediction accuracy still needs to be improved because of the false interactions of PPI data.In this paper,a novel approach is proposed to identify essential proteins from the PPI network by applying the Page Rank model as well as Integrating gene Expression profiles,gene Ontology and protein Complexes information(named PR_EOC).Distinguished from other approaches,to detect essential proteins,PR_EOC filters the PPI network by deleting these unreliable interactions firstly,and besides network topology and biological information data,we calculate the degrees of proteins in complexes and estimate the proteins importance in the whole PPI network by calculating Betweeness Centrality(BC).Moreover,it takes the neighbors characteristics into account by adopting PageRank algorithm.The computational experiments show that our approach PR_EOC is superior to the other eight state-of-the-art methods(DC,SC,IC,LAC,NC,SoECC,WDC,PeC)for predicting essential proteins in PPI network.
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