Effect of Data Standardization on Chemical Clustering and Similarity Searching

Abstract

Standardization is used to ensure that the variables in a similarity calculation make an equal contribution to the computed similarity value. This paper compares the use of seven different methods that have been suggested previously for the standardization of integer-valued or real-valued data, comparing the results with unstandardized data. Sets of structures from the MDL Drug Data Report and IDAlert databases and represented by Pipeline Pilot physicochemical parameters, molecular holograms and Molconn-Z parameters are clustered using the k-means and Ward's clustering methods. The resulting classifications are evaluated in terms of the degree of clustering of active compounds selected from eleven different biological activity classes, with these classes also being used in similarity searches. It is shown that there is no consistent pattern when the various standardization methods are ranked in order of decreasing effectiveness and that there is no obvious performance benefit (when compared to unstandardized data) that is likely to be obtained from the use of any particular standardization method.

Combining multiple classifications of chemical structures using consensus clustering

“…Our experiments here used two sets of molecules and activity classes that we have studied previously in a comparison of standardisation methods for clustering and similarity searching 32 . These datasets .…”

A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification

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Keep the conversations going: engagement-based customer segmentation on online social service platforms

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