Identifying Hub Proteins and Their Essentiality from Protein-protein Interaction Network

S. A. Bakar, J. Taheri, Albert Y. Zomaya
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引用次数: 2

Abstract

The study on protein-protein interactions is rapidly increasing; one of the most important findings of such study is the observation of hub proteins that play vital roles in all organisms. Identifying hub proteins may provide more information on essential proteins and lead to more efficient methods for their prediction. Here, we proposed a new network topological-based method for prediction of hub proteins in Saccharomyces cerevisiae (baker’s yeast). The method, HPNN (Hub Protein Prediction using Probabilistic Neural Network), has successfully predicts the hub proteins with accuracy of 95% (sensitivity of 1.0 and specificity of 0.89). Keywords-hub proteins, machine learning algorithms, neural networks, protein-protein interaction network.
从蛋白-蛋白相互作用网络中鉴定枢纽蛋白及其必要性
蛋白质-蛋白质相互作用的研究正在迅速增加;这项研究最重要的发现之一是观察到在所有生物体中起重要作用的枢纽蛋白。鉴定枢纽蛋白可以提供更多关于必需蛋白的信息,并为其预测提供更有效的方法。在这里,我们提出了一种新的基于网络拓扑的方法来预测酿酒酵母(面包酵母)的枢纽蛋白。该方法,HPNN (Hub Protein Prediction using Probabilistic Neural Network),成功地预测了枢纽蛋白,准确率为95%(灵敏度为1.0,特异性为0.89)。关键词:枢纽蛋白,机器学习算法,神经网络,蛋白-蛋白相互作用网络。
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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