Regression-based scoring

Chemscore Cscore Regression-based scoring function Suite of scoring functions (ChemScore, DOCK, FlexX, GOLD, PMF) and consensus scoring tool... 

FlexX Automated docking program with incremental construction algorithm and regression-based scoring function... 

SLIDE Automated docking program with multilevel and mean field algorithm and regression-based scoring function 189 www.bch.msu.edu/labs/kuhn/web/projects/slide/ home.html... 

X-SCORE Suite of three regression-based scoring functions and consensus scoring tool General modeling programs 190 swl6.im.med.umich.edu/software/xtool/... 

In the derivation of regression-based empirical scoring schemes, on the other hand, penalty terms have traditionally not been included. However, some situations like obvious electrostatic and steric clashes can be avoided by guessing reasonable penalty terms or by importing them from molecular mechanics force fields. An example of this is the chemical scoring function available in the docking program This function is a modified van... 

In step (ii) any multi-way regression model may be used and tested. Usually, different model types (e.g. Af-PLS and Tucker3-based regression on scores model), or models with a different number of components (e.g. a two-component Af-PLS model and a three-component W-PLS model) are tested. To have complete independence of and y, the matrices involved in building the model have to be preprocessed based on interim calibration data each time step (ii) is entered. 

Fig. 8 Comparison of BIOWIN PSM output with experimental soil half-lives for 38 pesticides and pesticide transformation products. In addition, three possible methods for translating BIOWIN PSM output into actual half-lives are also indicated EPISuite translation rules with modifications for PSM scores <2.25 as suggested in [60] (indicated as EPISuite Soil ), the Arnot et al. [61] regression and our own regression based on the pesticide data. The finely dashed lines indicate uncertainty intervals of a factor of ten around the EPISuite translation rules. Reprinted with permission from [62], p 688. (2006) Swiss Chemical Society...

PCR is based on a PCA input data transformation that by definition is independent of the Y-data set. The approach to defining the X-Y relationship is therefore accomplished in two steps. The first is to perform PCA on the. Y-data, yielding a set of scores for each measurement vector. That is, if xk is the fcth vector of d measurements at a time k, then zk is the corresponding kth vector of scores. The score matrix Z is then regressed onto the Y data, generating the predictive model... 

Principal component analysis (PCA) of the soil physico-chemical or the antibiotic resistance data set was performed with the SPSS software. Before PCA, the row MPN values were log-ratio transformed (ter Braak and Smilauer 1998) each MPN was logio -transformed, then, divided by sum of the 16 log-transformed values. Simple linear regression analysis between scores on PCs based on the antibiotic resistance profiles and the soil physico-chemical characteristics was also performed using the SPSS software. To find the PCs that significantly explain variation of SFI or SEF value, multiple regression analysis between SFI or SEF values and PC scores was also performed using the SPSS software. The stepwise method at the default criteria (p=0.05 for inclusion and 0.10 for removal) was chosen. 

Multiple regression analysis between the SFI or the SEF values and the PC scores gave the following formulae that describe the land degradation gradient based on the antibiotic resistance profiles. 

Table 5. Linear regression between principal component scores antibiotic resistance MPNs and soil physico-chemical characteristics based on the...

The variables 17, Ua, and are the corresponding uncertainty values for each parameter. They are computed to the 67% confidence interval by taking the standard error of each parameter in the regressions (i.e., ai, U2 and (73, and multiplying by their Student f-score ts (i.e., =ts SEo ), where ts is the Student t-score at the confidence level of interest and SEai is the corresponding standard error for the parameter ai. The period can be chosen based on the maximum value or another statistical parameter. The results of four experiments are given in Figure 9.8. 

One of the major uses of multivariate techniques has been the discrimination of samples based on sensory scores, which also has been found to provide information concerning the relative importance of sensory attributes. Techniques used for sensory discrimination include factor analysis, discriminant analysis, regression analysis, and multidimensional scaling (8, 10-15). 

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