论文标题
用条件随机场阐明膜蛋白的结构
Unravelling the Architecture of Membrane Proteins with Conditional Random Fields
论文作者
论文摘要
在本文中，我们将证明最近引入的图形模型：条件随机字段（CRF）提供了一个模板，将有关生物实体的微观信息整合到数学模型中，以了解其宏观级别的行为。更具体地说，我们将将CRF模型应用于蛋白质科学的重要分类问题，即基于观察到的一级结构的蛋白质的二级结构预测。基准数据集与28种其他方法的比较表明，CRF模型不仅可以实现极其准确的预测，而且可以进行模型的模块化性质以及整合不同信息来源的自由，这使该模型成为一种极其多功能的工具，可以在BioInformantics中解决许多其他问题。
In this paper, we will show that the recently introduced graphical model: Conditional Random Fields (CRF) provides a template to integrate micro-level information about biological entities into a mathematical model to understand their macro-level behavior. More specifically, we will apply the CRF model to an important classification problem in protein science, namely the secondary structure prediction of proteins based on the observed primary structure. A comparison on benchmark data sets against twenty-eight other methods shows that not only does the CRF model lead to extremely accurate predictions but the modular nature of the model and the freedom to integrate disparate, overlapping and non-independent sources of information, makes the model an extremely versatile tool to potentially solve many other problems in bioinformatics.
