自校正重加权采样法筛选抗结核活性预测的重要分子参数
Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity
摘要：
本文提出了一种新的描述子选择方法--自调整加权抽样法（STRS），用于选择磺胺类药物数据的重要描述子的最佳组合。描述符被定义为在多元线性回归模型如偏最小二乘（PLS）中具有大绝对系数的描述符。在这项研究中，PLS模型的回归系数的绝对值被用来作为一个指标来评估每个描述符的重要性，然后，基于每个描述符的重要性水平，STRS顺序选择N个子集的描述符从N个蒙特卡罗（MC）抽样运行在迭代和竞争的方式。在每次抽样中，首先随机选择固定比例（例如80%）的样本，以建立回归模型。然后，基于回归系数，采用基于快速递减函数（RDF）的强制描述子选择和基于自调整采样（STS）的竞争描述子选择两步过程来选择重要的特征向量。在运行循环之后，获得多个描述符子集，并计算用描述符子集建立的PLS模型的交叉验证均方根误差（RMSECV）。具有最低RMSECV的描述符子集被认为是最佳描述符子集。通过磺胺衍生物数据集对算法的性能进行了评估。结果表明，STRS的一个很好的特点，它通常可以找到一个最佳组合的一些重要的描述符，这是解释生物的利益。此外，我们的研究表明，更好的预测是由STRS相比，全描述集PLS建模，蒙特卡罗无信息变量消除（MC-UVE）。
英文摘要：
In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model such as partial least squares(PLS). In this study, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each descriptor Then, based on the importance level of each descriptor, STRS sequentially selects N subsets of descriptors from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a regresson model. Next, based on the regression coefficients, a two-step procedure including rapidly decreasing function (RDF) based enforced descriptor selection and self tuned sampling (STS) based competitive descriptor selection is adopted to select the important descriptorss. After running the loops, a number of subsets of descriptors are obtained and root mean squared error of cross validation (RMSECV) of PLS models established with subsets of descriptors is computed. The subset of descriptors with the lowest RMSECV is considered as the optimal descriptor subset. The performance of the proposed algorithm is evaluated by sulfanomide derivative dataset. The results reveal an good characteristic of STRS that it can usually locate an optimal combination of some important descriptors which are interpretable to the biologically of interest. Additionally, our study shows that better prediction is obtained by STRS when compared to full descriptor set PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE).
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