Multi-view spectral clustering and its chemical application
by Adeshola A. Adefioye; Xinhai Liu; Bart De Moor
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 6, No. 1/2, 2013

Abstract: Clustering is an unsupervised method that allows researchers to group objects and gather information about their relationships. In chemoinformatics, clustering enables hypotheses to be drawn about a compound's biological, chemical and physical property in comparison to another. We introduce a novel improved spectral clustering algorithm, proposed for chemical compound clustering, using multiple data sources. Tensor-based spectral methods, used in this paper, provide chemically appropriate and statistically significant results when attempting to cluster compounds from both the GSK-Chembl Malaria data set and the Zinc database. Spectral clustering algorithms based on the tensor method give robust results on the mid-size compound sets used here. The goal of this paper is to present the clustering of chemical compounds, using a tensor-based multi-view method which proves of value to the medicinal chemistry community. Our findings show compounds of extremely different chemotypes clustering together, this is a hint to the chemogenomics nature of our method.

Online publication date: Thu, 18-Sep-2014

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