R-NN curves:: An intuitive approach to outlier detection using a distance based method
R-NN curves:: An intuitive approach to outlier detection using a distance based method
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DOI：
10.1021/ci060013h
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发表时间：
2006-07-24
影响因子：
5.6
通讯作者：
Chen, Ting
中科院分区：
文献类型：
--
作者：
Guha, Rajarshi;Dutta, Debojyoti;Chen, Ting
化学结构库用于各种化学信息学任务，如虚拟筛选和QSAR建模，通常使用分子描述符进行表征。当使用库时，了解化合物在由一组描述符定义的空间中的分布是有用的。我们提出了一个简单的方法来分析的化合物在图书馆中的空间分布一般和离群值检测，特别是基于一系列的增加半径内的邻居的计数。由此产生的曲线，称为R-NN曲线，似乎遵循逻辑模型的任何给定的描述符空间，我们证明理论上的二维情况。该方法可应用于任意维数的数据集。R-NN曲线提供了一种视觉方法，可以轻松地检测位于给定描述符空间稀疏区域中的化合物。我们还提出了一种对R-NN曲线进行数值表征的方法，从而可以识别单个图中的异常值。
Libraries of chemical structures are used in a variety of cheminformatics tasks such as virtual screening and QSAR modeling and are generally characterized using molecular descriptors. When working with libraries it is useful to understand the distribution of compounds in the space defined by a set of descriptors. We present a simple approach to the analysis of the spatial distribution of the compounds in a library in general and outlier detection in particular based on counts of neighbors within a series of increasing radii. The resultant curves, termed R-NN curves, appear to follow a logistic model for any given descriptor space, which we justify theoretically for the 2D case. The method can be applied to data sets of arbitrary dimensions. The R-NN curves provide a visual method to easily detect compounds lying in a sparse region of a given descriptor space. We also present a method to numerically characterize the R-NN curves thus allowing identification of outliers in a single plot.