Selwood data set

So and Karplus [51] have developed a hybrid method that combines a GA for descriptor selection with an artificial neural network for model building. They found improved models for the Selwood data set when compared with the GFA and evolutionary programming methods, with the success being attributed to the ability of the neural network to select nonlinear descriptors. 

In this section, an exploratory data analysis of the Selwood data set (34) is carried out. The data set was downloaded from http //michem.disat.unimib.it/chm/download/datasets.htm. It... 

Table 4. Top 10 models obtained after variable selection of the Selwood data set in (35)...

Fig. 21. Structural and Variance Information (SVI) plot of in vitro antifilarial activity (-LOGEC50). The data set considered combines -LOGEC50 with the complete set of descriptors of the Selwood dataset.

Fig. 24. MEDA matrix of the PCA model with 10 PCs from the data set which combines the in vitro antifilarial activity (-LOGEC50) with the complete set of descriptors of the Selwood dataset. Two common factors are highlighted. The first one is mainly found in descriptors 1 to 3,5 to 7,17, 52 and 53. The second one is mainly foimd in descriptors 35,36,38 to 40,45,47 and 50. Though the second common factor is not present in -LOGEC50, it is in LOGP.

Variable Selection in Multiple Linear Regression Analysis of Selwood et al. Data Set—A Case Study. 

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