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A Cross-entropy-based Method for Essential Protein Identification in Yeast Protein-protein Interaction Network
Current Bioinformatics ( IF 4 ) Pub Date : 2021-05-01 , DOI: 10.2174/1574893615999201116210840
Weimiao Sun 1 , Lei Wang 1 , Jiaxin Peng 2 , Zhen Zhang 3 , Tingrui Pei 4 , Yihong Tan 1 , Xueyong Li 1 , Zhiping Chen 1
Affiliation  

Background: Research has shown that essential proteins play important roles in the development and survival of organisms. Because of the high costs of traditional biological experiments, several computational prediction methods based on known protein-protein interactions (PPIs) have been recently proposed to detect essential proteins.

Objective: Here, a novel prediction model called IoMCD is proposed to identify essential proteins by combining known PPIs with a variety of biological information about proteins, including gene expression data and homologous information of proteins.

Methods: Compared to the traditional state-of-the-art prediction models, IoMCD involves two kinds of weights that are obtained, respectively, by extracting topological features of proteins from the original known protein–protein interaction (PPI) networks and calculating the Pearson correlation coefficients (PCCs) between the gene expression data of proteins. Based on these two kinds of weights and adopting a cross-entropy method, a unique weight is assigned to each protein. Subsequently, the homologous information of proteins is used to calculate an initial score for each protein. Finally, based on the unique weights and initial score of proteins, an iterative method is designed to measure the essentialities of proteins.

Results: Intensive experiments were performed, and simulation results showed that the prediction accuracy of IoMCD, based on the dataset downloaded from the DIP and Gavin databases, was 92.16% and 89.71%, respectively, in the top 1% of the predicted essential proteins.

Conclusion: Both simulation results demonstrated that IoMCD can achieve excellent prediction accuracy and could be an effective method for essential protein prediction.



中文翻译:

一种基于交叉熵的酵母蛋白质-蛋白质相互作用网络中必需蛋白质鉴定方法

背景:研究表明,必需蛋白质在生物体的发育和生存中起着重要作用。由于传统生物实验的高成本,最近提出了几种基于已知蛋白质-蛋白质相互作用 (PPI) 的计算预测方法来检测必需蛋白质。

目的:在这里,提出了一种称为 IoMCD 的新型预测模型,通过将已知的 PPI 与蛋白质的各种生物学信息(包括基因表达数据和蛋白质的同源信息)相结合来识别必需蛋白质。

方法:与传统的最先进的预测模型相比,IoMCD 涉及两种权重,分别通过从原始已知的蛋白质 - 蛋白质相互作用(PPI)网络中提取蛋白质的拓扑特征并计算 Pearson蛋白质基因表达数据之间的相关系数(PCC)。基于这两种权重,采用交叉熵的方法,为每个蛋白质分配一个唯一的权重。随后,蛋白质的同源信息用于计算每个蛋白质的初始分数。最后,基于蛋白质的独特权重和初始分数,设计了一种迭代方法来衡量蛋白质的重要​​性。

结果:进行了密集的实验,仿真结果表明,基于从 DIP 和 Gavin 数据库下载的数据集,IoMCD 的预测精度分别为 92.16% 和 89.71%,在预测的必需蛋白质的前 1% 中。

结论:这两个模拟结果都表明 IoMCD 可以达到出色的预测精度,可以成为一种有效的必需蛋白质预测方法。

更新日期:2021-05-01
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