Model representation, data mining methods

Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50(2) 205-216... 

For the PCA and PLS-DA, sparse analyses perform a selection from automatic variables. More recently, more complex methods of automatic learning from data mining have been applied to metabolomic data. Decision trees aid the automatic selection of discriminant variables, supply a simple representation of the decision model (the tree) and constitute an exploratory technique to understand complex metabolic profiles. The artificial neuron network was successfully used to classify chemical profiles and is becoming one of the most popular methods for understanding patterns. Data visualization and interactivity are now used to visualize metabolomic data in order to facilitate the interpretation of complex data-sets. XCMS online [GOW 14] offers cloud-plots, PCA and interactive heatmaps (i.e. the heatmaps are graphical representations of correlation matrices). These two types of visualization help the user personalize the display and easily select the most interesting compounds. 

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