| 1. |
- Azmi, Jahanara, et al.
(författare)
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Metabolic trajectory characterisation of xenobiotic-induced hepatotoxic lesions using statistical batch processing of NMR data : Nicholson Jeremy K., Holmes Elaine
- 2002
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Ingår i: Analyst. - : Royal Society of Chemistry (RSC). ; 127, s. 271-6
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical batch processing (BP) analysis of 1H NMR urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin, -naphthylisothiocyanate (ANIT). ANIT (100 mg kg-1) was administered orally to rats (n = 5) and urine samples were collected from dosed and matching control rats at time-points up to 168 h post-dose. Urine samples were measured via1H NMR spectroscopy and partial least squares (PLS) based batch processing analysis was used to interpret the spectral data, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile over the 7 day study period was established, together with model confidence limits (±3 standard deviation), for the control group. Samples obtained from ANIT treated animals were evaluated using the control model. Time-dependent deviations from the control model were evident in all ANIT treated animals consisting of glycosuria, bile aciduria, an initial decrease in taurine levels followed by taurinuria and a reduction of tricarboxylic acid cycle intermediate excretion. BP provided an efficient means of visualising the biochemical response to ANIT in terms of both inter-animal variation and net variation in metabolite excretion profiles. BP also allowed multivariate statistical limits for normality to be established and provided a template for defining the sequence of time-dependent metabolic consequences of toxicity in NMR based metabonomic studies.
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| 2. |
- Bergman, Mats, 1964-, et al.
(författare)
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Strategic investments in the pulp and paper industry : a count data regression analysis
- 2000
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The effects of price and market size variables on the investment propensities in the pulp and paper industry are analyzed. A panel of 15 European countries for the time period 1984 - 1997 is used in the regression analysis. We find that the wages, the $US/ECU$ exchange rate, the price of paper and the installed production capacity are the main determinants of strategic investments in this industry. There are no - or only very small - effects from our measures of market size.
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| 3. |
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| 4. |
- 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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| 5. |
- Eriksson, Lennart, et al.
(författare)
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Megavariate analysis of hierarchical QSAR data
- 2002
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Ingår i: Journal of Computer-Aided Molecular Design. ; 16:10, s. 711-26
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate.A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called `super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated.In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.
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| 6. |
- Eriksson, Lennart, et al.
(författare)
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Multivariate biological profiling and principal toxicity regions of compounds: the PCB case study
- 2002
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 16:8-10, s. 497-509
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Tidskriftsartikel (refereegranskat)abstract
- The chemometric QSAR strategy, as applied in environmental sciences and drug design, is based on (1) multivariate characterization of chemical structure, (2) multivariate design in the principal properties of a set of compounds to select a representative training set, and (3) multivariate modelling of the structure-activity relationships. A multivariate QSAR investigation is often commenced by applying a screening design, and the selected compounds are tested biologically in a broad battery of test systems (multivariate biological profiling). In many cases the result is such that for certain biological end-points only some of the tested compounds are active, while for another set of biological end-points other tested chemicals are active. In other words, when looking at the chemical property space, there may be both responding and non-responding toxicity regions, or even regions of very specific toxicity mechanisms. This may lead to loss of resolution and balance in the resulting QSAR models. Therefore it might sometimes be worthwhile to focus the QSAR modelling on parts of the chemical space where high toxicity is expected or known to be the case. In this paper we describe a multi-stage modification of the chemometric QSAR strategy, aimed at identifying focused sets of compounds that provide a good mapping of such principal toxicity regions. This strategy is based on PCA, PLS and multivariate design in several stages. The strategy is illustrated using a data set of polychlorinated biphenyls, a set of compounds for which seven biological end-points were determined. Copyright © 2002 John Wiley & Sons, Ltd.
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| 7. |
- 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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| 8. |
- 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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| 9. |
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| 10. |
- Eriksson, Lennart, et al.
(författare)
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Three-block bi-focal PLS (3BIF-PLS) and its application in QSAR
- 2004
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Ingår i: SAR and QSAR in Environmental Research. - : Informa UK Limited. - 1062-936X .- 1029-046X. ; 15:5 & 6, s. 481-99
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Tidskriftsartikel (refereegranskat)abstract
- When X and Y are multivariate, the two-block partial least squares (PLS) method is often used. In this paper, we outline an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y. We have called this approach three-block bi-focal PLS (3BIF-PLS). It views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the X/Y association. Two data sets are used to illustrate 3BIF-PLS. Example I relates to single point mutants of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 and their ability to transform halogenated hydrocarbons, some of which are found as organic pollutants in soil. Example II deals with soil remediation capability of bacteria. Whole bacterial communities are monitored over time using "DNA-fingerprinting" technology to see how pollution affects population composition. Since the data sets are large, hierarchical multivariate modelling is invoked to compress data prior to 3BIF-PLS analysis. It is concluded that the 3BIF-PLS approach works well. The paper contains a discussion of pros and cons of the method, and hints at further developmental opportunities.
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