Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1002/qsar.200860065 |
| Titel (primär) | Estimation of soil organic carbon normalized sorption coefficient (Koc) using least squares-support vector machine |
| Autor | Wang, B.; Chen, J.; Li, X.; Wang, Y.; Chen, L.; Zhu, M.; Yu, H.; Kühne, R.
; Schüürmann, G. |
| Quelle | QSAR & Combinatorial Science |
| Erscheinungsjahr | 2009 |
| Department | OEC |
| Band/Volume | 28 |
| Heft | 5 |
| Seite von | 561 |
| Seite bis | 567 |
| Sprache | englisch |
| Keywords | LS-SVM; Soil organic carbon normalized sorption coefficient; Parameters determination; Adaptive random search technique; QSAR |
| Abstract | Least squares-support vector machine (LS-SVM) was used to derive a quantitative structure-activity relationship (QSAR) model for predicting the soil sorption coefficient normalized to organic carbon, Koc, from 24 fragment-specific increments and four further molecular descriptors, employing a training set of 571 organic compounds and three external validation sets. The combinational parameters of LS-SVM were optimized by adaptive random search technique (ARST). ARST could search the optimal combinational parameters of LS-SVM from the solution space in a simple and quick way. The developed LS-SVM model was compared with the model established by multiple linear regression (MLR) analysis using the same data sets. Generally, the LS-SVM model performed slightly better than the MLR model with respect to goodness-of-fit, predictivity, and applicability domain (AD). The ADs of the LS-SVM and MLR models were described on the basis of leverages and standardized residuals. Both the LS-SVM and MLR models had wide ADs within a given reliability (standardized residual<3 SE units), but the LS-SVM model was superior for compounds with high leverages. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=691 |
| Wang, B., Chen, J., Li, X., Wang, Y., Chen, L., Zhu, M., Yu, H., Kühne, R., Schüürmann, G. (2009): Estimation of soil organic carbon normalized sorption coefficient (Koc) using least squares-support vector machine QSAR Comb. Sci. 28 (5), 561 - 567 10.1002/qsar.200860065 |
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