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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Predicting Essential Proteins Basedon Weighted Degree Centrality
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Predicting Essential Proteins Basedon Weighted Degree Centrality

机译：基于加权中心度的基本蛋白质预测

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摘要

Essential proteins are vital for an organism’s viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and edge clustering coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI and e-Coli networks in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The prediction results and comparative analyses are shown in the paper. Furthermore, the parameter $lambda$ in the method WDC will be analyzed in detail and an optimal $lambda$ value will be found. Based on the optimal $lambda$ value, the differentiation of WDC and another prediction method PeC is discussed. The analyses prove that WDC outperforms other methods including DC, BC, CC, SC, EC, IC, NC, and PeC. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.
机译：必需蛋白质对于生物在各种条件下的生存能力至关重要。已经开发出许多实验和计算方法来鉴定必需蛋白质。由于PPI数据不足，基于全局蛋白质-蛋白质相互作用（PPI）网络进行必需蛋白质的计算预测受到严格限制，但幸运的是，基因表达谱有助于弥补这一不足。在这项工作中，皮尔逊相关系数（PCC）用于弥合PPI和基因表达数据之间的差距。基于PCC和边缘聚类系数（ECC），开发了一种新的中心度度量，即加权度中心度（WDC），以实现对必需蛋白质的可靠预测。 WDC用于鉴定酵母PPI和e-Coli网络中的必需蛋白，以评估其性能。为了进行比较，还进行了其他预测技术来鉴定必需蛋白。一些评估方法用于分析各种预测方法的结果。给出了预测结果和对比分析。此外，将详细分析方法WDC中的参数$ lambda $并找到最佳的$ lambda $值。基于最优的λ值，讨论了WDC的微分和另一种预测方法PeC。分析证明WDC优于其他方法，包括DC，BC，CC，SC，EC，IC，NC和PeC。同时，分析还意味着这是通过整合不同数据源来预测必需蛋白质的有效方法。

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