Consensus principal component analysis

The molecular interaction fields were subjected to multivariate data analysis using consensus principal component analysis (cPCA). 

They can be used directly in protein families in order to perform selectivity analysis based on consensus principal component analysis methodology. This technique helps to identify regions in the protein space that are selective for one enzyme. The selective region may describe the selective pattern of the target proteins. Moreover, this information can be used to compare different models of the same enzyme. 

Figure 19.2 Principal component analysis (PCA) on the consensus from the six perfumers obtained by GPA. Interpretation of the individual attributes led to nine semantic items represented on the map. An item was obtained when there were several similar individual attributes highly correlated. Ellipses represent product clusters after HCA.

Initially an optimised model was constructed using the data collected as outlined above by constructing a principal component (PC)-fed linear discriminant analysis (LDA) model (described elsewhere) [7, 89], The linear discriminant function was calculated for maximal group separation and each individual spectral measurement was projected onto the model (using leave-one-out cross-validation) to obtain a score. The scores for each individual spectrum projected onto the model and colour coded for consensus pathology are shown in Fig. 13.3. The simulation experiments used this optimised model as a baseline to compare performance of models with spectral perturbations applied to them. The optimised model training performance achieved 93% accuracy overall for the three groups. 

LDA, linear discriminant analysis NN, neural nets P, principal component (PC-based) CCD, computerized consensus diagnosis N, normalized U, unnormalized R, subregion-based F, fuzzy. 

CMDand CMCindicescanbeusefulintheevaluationofthediversityof —> chemical spaces in molecule library design, in selecting the optimal number of significant principal components in PCA, and in selecting the most diverse QSAR models for —> consensus analysis. 

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