Department of Computer Science and Engineering, York University, ON, Toronto, Canada.
BMC Bioinformatics. 2010 Jan 18;11 Suppl 1(Suppl 1):S64. doi: 10.1186/1471-2105-11-S1-S64.
One of key issues in the post-genomic era is to assign functions to uncharacterized proteins. Since proteins seldom act alone; rather, they must interact with other biomolecular units to execute their functions. Thus, the functions of unknown proteins may be discovered through studying their interactions with proteins having known functions. Although many approaches have been developed for this purpose, one of main limitations in most of these methods is that the dependence among functional terms has not been taken into account.
We developed a new network-based protein function prediction method which combines the likelihood scores of local classifiers with a relaxation labelling technique. The framework can incorporate the inter-relationship among functional labels into the function prediction procedure and allow us to efficiently discover relevant non-local dependence. We evaluated the performance of the new method with one other representative network-based function prediction method using E. coli protein functional association networks.
Our results showed that the new method has better prediction performance than the previous method. The better predictive power of our method gives new insights about the importance of the dependence between functional terms in protein functional prediction.
在后基因组时代，一个关键问题是为未知蛋白赋予功能。由于蛋白质很少单独起作用；相反，它们必须与其他生物分子单元相互作用才能执行其功能。因此，未知蛋白的功能可以通过研究它们与具有已知功能的蛋白的相互作用来发现。尽管为此目的已经开发了许多方法，但这些方法中的大多数的一个主要局限性是没有考虑功能术语之间的依赖性。
我们开发了一种新的基于网络的蛋白功能预测方法，该方法将局部分类器的似然评分与松弛标记技术相结合。该框架可以将功能标签之间的关系纳入功能预测过程，并允许我们有效地发现相关的非局部依赖性。我们使用大肠杆菌蛋白功能关联网络评估了新方法与另一种有代表性的基于网络的功能预测方法的性能。
我们的结果表明，新方法的预测性能优于以前的方法。我们方法的更好预测能力提供了关于蛋白功能预测中功能术语之间依赖性重要性的新见解。
