| 1. |
- Eriksson, Lennart, et al.
(författare)
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GIFI-PLS: Modeling of Non-Linearities and Discontinuities in QSAR
- 2000
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Ingår i: QSAR. ; 19:4, s. 345-55
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces to the QSAR community a novel method for modeling and understanding non-linear relationships between biological potency and chemical structure properties of molecules. The approach, GIFI-PLS, is based on ``binning'' of quantitative X-variables into categorical variables. Each categorical variable is then expanded into a set of linked 1/0 dummy variables, which enable modeling of non-linearity. By way of four QSAR data sets, it is demonstrated that GIFI-PLS is useful for modeling of non-linearity and discontinuity in QSAR, and that the predictive power of a QSAR model may improve.
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| 2. |
- Eriksson, Lennart, et al.
(författare)
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On the selection of the training set in environmental QSAR analysis when compounds are clustered
- 2000
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Ingår i: Journal of Chemometrics. ; 14:5-6, s. 599-616
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Tidskriftsartikel (refereegranskat)abstract
- In QSAR analysis in environmental sciences, adverse effects of chemicals released to the environment are modelled and predicted as a function of the chemical properties of the pollutants. Usually the set of compounds under study contains several classes of substances, i.e. a more or less strongly clustered set. It is then needed to ensure that the selected training set comprises compounds representing all those chemical classes. Multivariate design in the principal properties of the compound classes is usually appropriate for selecting a meaningful training set. However, with clustered data, often seen in environmental chemistry and toxicology, a single multivariate design may be suboptimal because of the risk of ignoring small classes with few members and only selecting training set compounds from the largest classes. Recently a procedure for training set selection recognizing clustering was proposed by us. In this approach, when non-selective biological or environmental responses are modelled, local multivariate designs are constructed within each cluster (class). The chosen compounds arising from the local designs are finally united in the overall training set, which thus will contain members from all clusters. The proposed strategy is here further tested and elaborated by applying it to a series of 351 chemical substances for which the soil sorption coefficient is available. These compounds are divided into 14 classes containing between 10 and 52 members. The training set selection is discussed, followed by multivariate QSAR modelling, model interpretation and predictions for the test set. Various types of statistical experimental designs are tested during the training set selection phase.
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| 3. |
- Eriksson, Lennart, et al.
(författare)
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Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data
- 2000
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Ingår i: Analytica Chimica Acta. ; 420:2, s. 181-95
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, multivariate calibration of complicated process fluorescence data is presented. Two data sets related to the production of white sugar are investigated. The first data set comprises 106 observations and 571 spectral variables, and the second data set 268 observations and 3997 spectral variables. In both applications, a single response, ash content, is modelled and predicted as a function of the spectral variables. Both data sets contain certain features making multivariate calibration efforts non-trivial. The objective is to show how principal component analysis (PCA) and partial least squares (PLS) regression can be used to overview the data sets and to establish predictively sound regression models. It is shown how a recently developed technique for signal filtering, orthogonal signal correction (OSC), can be applied in multivariate calibration to enhance predictive power. In addition, signal compression is tested on the larger data set using wavelet analysis. It is demonstrated that a compression down to 4% of the original matrix size - in the variable direction - is possible without loss of predictive power. It is concluded that the combination of OSC for pre-processing and wavelet analysis for compression of spectral data is promising for future use.
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| 4. |
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