Towards better understanding of feature-selection or reduction techniques for Quantitative Structure-Activity Relationship models
Towards better understanding of feature-selection or reduction techniques for Quantitative Structure-Activity Relationship models
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DOI：
10.1016/j.trac.2012.09.008
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发表时间：
2013-01-01
影响因子：
13.1
通讯作者：
Vander Heyden, Yvan
中科院分区：
文献类型：
--
作者：
Goodarzi, Mohammad;Funar-Timofei, Simona;Vander Heyden, Yvan
定量构效关系（QSAR）是一种线性或非线性模型，它将分子描述符的变化与一系列活性和/或非活性分子的生物活性变化联系起来。在本文中，不同的特征选择或约简方法都在特征选择过程中与偏最小二乘（PLS）建模相结合。利用完整的分子描述符建立PLS模型，并以此作为参考，检验不同特征选择方法的可靠性和性能。为了评估不同特征选择方法的能力，他们在两个数据集上进行了测试。(c) 2012 Elsevier Ltd.版权所有。
A Quantitative Structure-Activity Relationship (QSAR) is a linear or non-linear model, which relates variations in molecular descriptors to variations in the biological activity of a series of active and/or inactive molecules.For this article, different feature-selection or reduction methods were all coupled with Partial Least Squares (PLS) modeling during the selection of features. A PLS model was also built with the entire set of molecular descriptors and was used as a reference to check the reliability and the performance of the different feature-selection methods. To evaluate the ability of the different feature-selection methods, they were performed on two data sets. (c) 2012 Elsevier Ltd. All rights reserved.