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Incorporating variable importance into kernel PLS for modeling the structure-activity relationship

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成果类型:
期刊论文
作者:
Huang, Xin;Luo, Yi-Ping;Xu, Qing-Song;Liang, Yi-Zeng
通讯作者:
Huang, X
作者机构:
[Huang, Xin; Luo, Yi-Ping] Hunan City Univ, Dept Math, Yiyang 413000, Peoples R China.
[Liang, Yi-Zeng] Cent S Univ, Coll Chem & Chem Engn, Changsha 410083, Hunan, Peoples R China.
[Xu, Qing-Song] Cent S Univ, Sch Math & Stat, Changsha 410075, Hunan, Peoples R China.
通讯机构:
[Huang, Xin] Hunan City Univ, Dept Math, Yiyang 413000, Peoples R China.
语种:
英文
关键词:
Kernel partial least squares (KPLS);Variable importance (VI);Kernel methods;Regression coefficients;Structure-activity relationship (SAR)
期刊:
Journal of Mathematical Chemistry
ISSN:
0259-9791
年:
2018
卷:
56
期:
3
页码:
713-727
文献类别:
WOS:Article
所属学科:
ESI学科类别:化学;WOS学科类别:Chemistry, Multidisciplinary;Mathematics, Interdisciplinary Applications
入藏号:
基金类别:
National Bureau of Statistics of P.R. China [2015LY79]; Hunan Provincial Natural Science Foundation of China [2016JJ2011]; Hunan Provincial Education Department of China [16C0295]
机构署名:
本校为第一且通讯机构
院系归属:
理学院
摘要:
Kernel partial least squares (KPLS) has become popular techniques for chemical and biological modeling, which is a nonlinear extension of linear PLS. Training samples are transformed into a feature space via a nonlinear mapping, and then PLS algorithm can be carried out in the feature space. However, one of the main limitations of KPLS is that each feature is given the same importance in the kernel matrix, thus explaining the poor performance of KPLS for data with many irrelevant features. In this study, we provide a new strategy incorporated variable importance into KPLS, which is termed as the WKPLS approach. The WKPLS approach by modifying the kernel matrix provides a feasible way to differentiate between the true and noise variables. On the basis of the fact that the regression coefficients of the PLS model reflect the importance of variables, we firstly obtain the normalized regression coefficients by establishing the PLS model with all the variables. Then, Variable importance is incorporated into primary kernel. The performance of WKPLS is investigated with one simulated dataset and two structure–activity relationship (SAR) datasets. Compared with standard linear kernel PLS and Gaussian kernel PLS, The results show that WKPLS yields superior prediction performances to standard KPLS. WKPLS could be considered as a good mechanism by introducing extra information to improve the performance of KPLS for modeling SAR.
参考文献:
C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, V. Prachayasittikul, A practical overview of quantitative structure-activity relationship. EXCLI J. , 1611–2156 (2009)
H. Sun, A naive Bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. J. Med. Chem. , 4031–4039 (2005)
J. Gola, O. Obrezanova, E. Champness, M. Segall, ADMET property prediction: the state of the art and current challenges. QSAR Comb. Sci. , 1172–1180 (2006)
S. Wold, H. Martens, H. Wold, The multivariate calibration problem in chemistry solved by the PLS method. Conf. Proc. Matrix Pencils , 286–293 (1983)
P. Geladi, B. Kowalski, Partial least-regression: a tutorial. Anal. Chim. Acta , 1–17 (1986)

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