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A topology-sharing based method for protein function prediction via analysis of protein functional association networks

机译:一种基于蛋白质功能预测的拓扑共享方法,通过分析蛋白质功能关联网络

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The sequencing of many genomes has brought to light the discovery of thousands of possible open reading frames which are potentially transcribed and translated into gene products, but a great majority of these have yet to be characterized. Since proteins seldom act alone; rather, they must interact with other biomolecular units to execute their functions. Thus, the function of unknown proteins may be discovered through studying their interaction with known proteins having known functions. Here, we developed a new network topology-based method in which the functions of the uncharacterized proteins can be predicted based on the functions of the proteins sharing similar topology structure. We explored to incorporate the inter-relationship among functional labels into the function prediction framework. We evaluated the performance of the new method with other representative network-based function prediction method using E. coli protein networks. Our results showed that the method has better prediction performance in E. coli protein function prediction.
机译:许多基因组的测序已经揭示了数千可能的开放阅读帧的发现,可能被视为转录并翻译成基因产品,但这些尚未表征了大多数。由于蛋白质很少独自行动;相反,它们必须与其他生物分子单元进行交互以执行其功能。因此,可以通过研究与具有已知功能的已知蛋白质的相互作用来发现未知蛋白质的功能。在这里,我们开发了一种基于新的网络拓扑结构,其中可以基于共享类似拓扑结构的蛋白质的功能来预测非特征蛋白的功能。我们探索了将功能标签之间的相互关系纳入函数预测框架。我们使用大肠杆菌蛋白质网络评估了新方法的性能与其他基于网络的功能预测方法的性能。我们的研究结果表明,该方法在大肠杆菌蛋白质功能预测中具有更好的预测性能。

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