Variable selection and modeling method based

VSMP Variable selection and modeling method based on the prediction... 

Narayanan and Gunturi [33] developed QSPR models based on in vivo blood-brain permeation data of 88 diverse compounds, 324 descriptors, and a systematic variable selection method called Variable Selection and Modeling method based on the Prediction (VSMP). VSMP efficiently explored all... 

VSMP A Novel Variable Selection and Modeling Method Based on the Prediction. 

In many modeling techniques, the number of parameters is modified many times looking for a setting that provides the maximum predictive ability for the model. Techniques for variable selection and methods based on artificial neural networks perform an optimization, that is, they search for conditions able to provide the maximum predictive ability possible for a given sample subset. 

The variable selection methods have been also adopted for region selection in the area of 3D QSAR. For example, GOLPE [31] was developed with chemometric principles and q2-GRS [32] was developed based on independent CoMFA analyses of small areas of near-molecular space to address the issue of optimal region selection in CoMFA analysis. Both of these methods have been shown to improve the QSAR models compared to original CoMFA technique. 

An important point is the evaluation of the models. While most methods select the best model at the basis of a criterion like adjusted R2, AIC, BIC, or Mallow s Cp (see Section 4.2.4), the resulting optimal model must not necessarily be optimal for prediction. These criteria take into consideration the residual sum of squared errors (RSS), and they penalize for a larger number of variables in the model. However, selection of the final best model has to be based on an appropriate evaluation scheme and on an appropriate performance measure for the prediction of new cases. A final model selection based on fit-criteria (as mostly used in variable selection) is not acceptable. 

The rather time- and cost-expensive preparation of primary brain microvessel endothelial cells, as well as the limited number of experiments which can be performed with intact brain capillaries, has led to an attempt to predict the blood-brain barrier permeability of new chemical entities in silico. Artificial neural networks have been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those of the blood from their structural parameters [117, 118]. A summary of the current efforts is given in Chap. 25. Quantitative structure-property relationship models based on in vivo blood-brain permeation data and systematic variable selection methods led to success rates of prediction of over 80% for barrier permeant and nonper-meant compounds, thus offering a tool for virtual screening of substances of interest [119]. 

Candidate Tariables were chosen using a mixed-variable selection method and validated based on prediction ability. Separate models (with different measurement variables) were estimated for each of the components. The final models and measures of performance are as follows (see Table 5.11 for a description of these figures of merit) ... 

A partially Bayesian approach was suggested by Chipman et al. (1997). They used independent prior distributions for each main effect being active. The prior distribution selected for Pj was a mixture of normals, namely, N(0, r ) with prior probability 1 — tzj and N(0, Cj if) with prior probability ttj, where Cj greatly exceeds 1. The prior distribution for a2 was a scaled inverse-x2. They then used the Gibbs-sampling-based stochastic search variable selection method of George and McCulloch (1993) to obtain approximate posterior probabilities for Pj, that is, for each factor they obtained the posterior probability that Pj is from /V(0, cj if) rather than from N(0, r ). They treated this as a posterior probability that the corresponding factor is active and used these probabilities to evaluate the posterior probability of each model. 

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