Identifying essential proteins via integration of protein interaction and gene expression data

Xiwei Tang, Jianxin Wang, Yi Pan
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引用次数: 14

Abstract

Essential proteins are vital for an organism's viability under a variety of conditions. 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 network 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 analyses prove that WDC outperforms other state-of-the-art ones. 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.
通过整合蛋白质相互作用和基因表达数据来鉴定必需蛋白质
必需蛋白质对生物体在各种条件下的生存能力至关重要。基于全局蛋白-蛋白相互作用(PPI)网络的必需蛋白的计算预测由于PPI数据的不足而受到严重限制,但幸运的是基因表达谱有助于弥补这一缺陷。在这项工作中,使用Pearson相关系数(PCC)来弥合PPI和基因表达数据之间的差距。在PCC和边缘聚类系数(ECC)的基础上,提出了一种新的中心性测度加权度中心性(WDC)来实现对必需蛋白的可靠预测。利用WDC识别酵母PPI网络中的必需蛋白,以评估其性能。为了比较,其他预测技术也被用于识别必需蛋白质。用一些评价方法对各种预测方法的结果进行了分析。分析证明,WDC优于其他最先进的技术。同时,这些分析也意味着通过整合不同的数据来源来预测必需蛋白质是一种有效的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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