论文标题
均匀闭合方法的性能作为贝叶斯型分类器开放打结
Performance of the Uniform Closure Method for open knotting as a Bayes-type classifier
论文作者
论文摘要
在蛋白质和其他大分子链中发现打结的发现使研究人员更仔细地考虑如何识别和分类开放弧中的结。大多数定义通过构造封闭的集合并测量这些封闭中不同结的概率来分类开口弧。在本文中，我们考虑将打结类型分配为分类问题，并将贝叶斯地图分类器的性能与标准统一闭合方法进行比较。令人惊讶的是，我们发现两种方法本质上都是分类器，在各种输入弧长度和结类型上具有可比的精度和正预测值。
The discovery of knotting in proteins and other macromolecular chains has motivated researchers to more carefully consider how to identify and classify knots in open arcs. Most definitions classify knotting in open arcs by constructing an ensemble of closures and measuring the probability of different knot types among these closures. In this paper, we think of assigning knot types to open curves as a classification problem and compare the performance of the Bayes MAP classifier to the standard Uniform Closure Method. Surprisingly, we find that both methods are essentially equivalent as classifiers, having comparable accuracy and positive predictive value across a wide range of input arc lengths and knot types.
