Relationship variable selection

Zheng W, Tropsha A. Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Comput Sci 2000 40(l) 185-94. 

D.M. Allen, The relationship between variable selection and data augmentation and a method for prediction. Technometrics, 16(1974) 125-127. 

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]. 

Quantitative Structure-Activity Relationship models are used increasingly in chemical data mining and combinatorial library design [5, 6]. For example, three-dimensional (3-D) stereoelectronic pharmacophore based on QSAR modeling was used recently to search the National Cancer Institute Repository of Small Molecules [7] to find new leads for inhibiting HIV type 1 reverse transcriptase at the nonnucleoside binding site [8]. A descriptor pharmacophore concept was introduced by us recently [9] on the basis of variable selection QSAR the descriptor pharmacophore is defined as a subset of... 

Rogers, D. Hopfingee, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 854-866. Kubinyi, H. Variable selection in QSAR studies. 1. An evolutionary algorithm. Quantum Struct.-Act. Relat. 1994, 13, 285-294. 

More innovative methods for examining relationships between individual LOE for the SQT include quantitative estimation of probability derived from odds ratio (Smith et al., 2002) and meta-analysis resulting in pooled, empirically derived P-values (Bailer et al., 2002). Comparison of odds ratio and meta-analysis with PCA for clustering sites into groups of similar impact (Reynoldson et al., 2002a) revealed similarities and differences. The differences between the three methods (PCA, odds ratio and meta-analysis) were ascribed to three factors, which almost certainly apply to all integrations the variables selected the manner in which information is combined within a LOE and, the statistical methodology employed. 

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. 

Kubinyi, H., Variable selection in QSAR studies, Quantitative Structure-Activity Relationships 13, 285-294, 1994. 

An inexperienced user or sometimes even an avid practitioner of QSAR could be easily con-fiased by the multitude of methodologies and naming conventions used in QSAR studies. Two-dimensional (2D) and three-dimensional (3D) QSAR, variable selection and artificial neural network methods, comparative molecular field analysis (CoMFA), and binary QSAR present examples of various terms that may appear to describe totally independent approaches, which cannot be even compared to each other. In fact, any QSAR method can be generally defined as the application of mathematical and statistical methods to the problem of finding empirical relationships (QSARmod-els)of the form, D . D ), where... 

For illustration, we shall consider here one of the nonlinear variable selection methods that adopts a k-Nearest Neighbor (kNN) principle to QSAR [kNN-QSAR (49)]. Formally, this method implements the active analog principle that lies in the foundation of the modern medicinal chemistry. The kNN-QSAR method employs multiple topological (2D) or topographical (3D) descriptors of chemical structures and predicts biological activity of any compound as the average activity of k most similar molecules. This method can be used to analyze the structure-activity relationships (SAR) of a large number of compounds where a nonlinear SAR may predominate. 

The general problem of excluding variables from data, i.e. of estimating the best I vector, can be divided in two main blocks methods for - variable reduction and methods for - variable selection. The first group of methods evaluates the variable exclusion by inner relationships among the p descriptor variables, i.e. 

Marengo, E., Carpignano, R., Savarino, P. and Viscardi, G. (1992). Comparative Study of Different Structural Descriptors and Variable Selection Approaches Using Partial Least Squares in Quantitative Structure-Activity Relationships. Chemom.Intell.Lab.Syst., 14,225-233. 

Waller, C.L. and Bradley, M.P. (1999). Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure-Activity Relationship Studies. J.Chem.lnfComput.ScL, 39,345-355. 

Zheng, W. and Tropsha, A. (2000). Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle. J.Chem.Inf.Comput.ScL, 40,185-194. 

A ranking model is a relationship between a set of dependent attributes, experimentally investigated, and a set of independent attributes, i.e. model variables. As in regression and classification models the variable selection is one of the main step to find predictive models. In the present work, the Genetic Algorithm (GA-VSS) approach is proposed as the variable selection method to search for the best ranking models within a wide set of predictor variables. The ranking based on the selected subsets of variables is... 

VI.17 We like to regulate the composition of distillate and bottom products from a distillation column, using the reflux flow R and vapor flow in the column V (or equivalently the steam flow rate) as manipulated variables. Select the pairings between controlled outputs and manipulated inputs so that the resulting loops offer minimum steady-state interaction. The following input-output relationships for the distillation column have been determined experimentally. 

How can parsimonious models be constructed There are several possible approaches, however in this chapter a combination of data compression and variable selection will be used. Data compression achieves parsimony through the reduction of the redundancy in the data representation. However, compression without involving information about the dependent variables will not be optimal. It is therefore suggested that variable selection should be performed on the compressed variables and not on the original variables which is the usual strategy. Variable selection has been applied with success in fields such as analytical chemistry [1-4], quantitative structure-activity relationships (QSAR) [5-8] and analytical biotechnology [9-11]. 

H. Kubinyi, Variable Selection in QSAR Studies 2 a Highly Efficient Combination of Systematic Search and Evolution, Quantitative Strueture-Activity Relationships, 13(4) (1994), 393-401. 

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