Variable Selection and Modeling method

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. 

Genetic programming, described earlier, picks only certain variables from the model. The rules, which may be in the form of a computer language such as Lisp, or easily interpretable equations, produce a formula from which a result can be calculated (e.g. if (measurement 1 >2.37 and measurement 2<0.53) or measurement 3 > 4.28 then sample is adulterated else sample is clean) [162 — 165]. Rather than being a pre-processing step before statistical analysis, this method combines the variable selection and model formation stages into one. 

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. 

Data preprocessing is important in multivariate calibration. Indeed, the relationship between even basic procedures such as centring the columns is not always clear, most investigators following conventional methods, that have been developed for some popular application but are not always appropriately transferable. Variable selection and standardisation can have a significant influence on the performance of calibration models. 

PCM modeling aims to find an empirical relation (a PCM equation or model) that describes the interaction activities of the biopolymer-molecule pairs as accurate as possible. To this end, various linear and nonlinear correlation methods can be used. Nonlinear methods have hitherto been used to only a limited extent. The method of prime choice has been partial least-squares projection to latent structures (PLS), which has been found to work very satisfactorily in PCM. PCA is also an important data-preprocessing tool in PCM modeling. Modeling includes statistical model-validation techniques such as cross validation, external prediction, and variable-selection and signal-correction methods to obtain statistically valid models. (For general overviews of modeling methods see [10]). 

The number of published accounts of variable selection methods in the general literature is enormous. To provide a focus, this section will concentrate just on applications to computer-aided drug design. Variable selection was identified as an important requirement at about the same time as the need for variable elimination techniques. The simplest method of variable selection is to choose those variables that have a large correlation with the response and, for simple datasets, that method is probably not a bad choice. As we have shown in this chapter, variable selection may be an integral part of a modeling technique, but not all modeling methods lend themselves to variable selection, and in these cases, other techniques need to be applied. 

If the chemical data (y) are noisy and the number of calibration samples low, then PLSR may or may not give an advantage over other methods. On the one hand, PLSR then overfits more easily than, for example, PCR, since the noisy y-variable is used more extensively in PLSR and in PCR. In that respect PLSR resembles SMLR, where y is used for both variable selection and parameter estimation. So the validation to determine the optimal number of factors is then very important in PLSR. On the other hand, we have observed that if theX data contain a lot of variability irrelevant for modeling y, then PLSR has a better chance, than, for example, PCR of extracting just the y-relevant X stmctures before over-fitting. 

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. 

When applied to QSAR studies, the activity of molecule u is calculated simply as the average activity of the K nearest neighbors of molecule u. An optimal K value is selected by the optimization through the classification of a test set of samples or by the leave-one-out cross-validation. Many variations of the kNN method have been proposed in the past, and new and fast algorithms have continued to appear in recent years. The automated variable selection kNN QSAR technique optimizes the selection of descriptors to obtain the best models [20]. 

Narayanan R and Gunturi SB. In silico ADME modelling prediction models for blood-brain barrier permeation using a systematic variable selection method. Bioorg Med Chem 2005 13 3017-28. 

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