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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Predicting Essential Proteins by Integrating Network Topology, Subcellular Localization Information, Gene Expression Profile and GO Annotation Data
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Predicting Essential Proteins by Integrating Network Topology, Subcellular Localization Information, Gene Expression Profile and GO Annotation Data

机译：通过集成网络拓扑，亚细胞定位信息，基因表达分布和GO注释数据来预测基本蛋白质

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摘要

Essential proteins are indispensable for maintaining normal cellular functions. Identification of essential proteins from Protein-protein interaction (PPI) networks has become a hot topic in recent years. Traditionally biological experimental based approaches are time-consuming and expensive, although lots of computational based methods have been developed in the past years; however, the prediction accuracy is still unsatisfied. In this research, by introducing the protein sub-cellular localization information, we define a new measurement for characterizing the protein's subcellular localization essentiality, and a new data fusion based method is developed for identifying essential proteins, named TEGS, based on integrating network topology, gene expression profile, GO annotation information, and protein subcellular localization information. To demonstrate the efficiency of the proposed method TEGS, we evaluate its performance on two Saccharomyces cerevisiae datasets and compare with other seven state-of-the-art methods (DC, BC, NC, PeC, WDC, SON, and TEO) in terms of true predicted number, jackknife curve, and precision-recall curve. Simulation results show that the TEGS outperforms the other compared methods in identifying essential proteins. The source code of TEGS is freely available at https://github.com/wzhangwhu/TEGS.
机译：基本蛋白是保持正常细胞功能的必由之可。近年来，来自蛋白质 - 蛋白质相互作用（PPI）网络的基本蛋白质的鉴定已成为一个热门话题。传统的生物实验基础的方法是耗时和昂贵的方法，尽管过去几年已经开发了许多基于计算的方法;然而，预测准确性仍然不满意。在本研究中，通过引入蛋白质亚细胞定位信息，我们定义了用于表征蛋白质的亚细胞定位基本度的新测量，并且基于集成网络拓扑结构识别名为TEG的基于新的数据融合的方法，基因表达谱，GO注释信息和蛋白质亚细胞定位信息。为了证明所提出的方法TEGS的效率，我们评估其在两种糖蜜酿酒座数据集上的性能，并与其他七种最先进的方法（DC，BC，NC，PEC，WDC，SON和TEO）进行比较真正的预测数字，千刀曲线和精密召回曲线。仿真结果表明，TEGS优于鉴定必需蛋白的其他比较方法。 TEGS的源代码在https://github.com/wzhangwhu/tegs自由使用。

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