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Interrelated Two-way Clustering (ITC) is an unsupervised clustering methoddeveloped to divide samples into two groups in gene expression data obtainedthrough microarrays, selecting important genes simultaneously in the process.This has been found to be a better approach than conventional clusteringmethods like K-means or self-organizing map for the scenarios when number ofsamples much smaller than number of variables (n<<p). In this paper we used theITC approach for classification of a diverse set of 508 chemicals regardingmutagenicity. A large number of topological indices (TIs), 3-dimensional, andquantum chemical descriptors, as well as atom pairs (APs) have been used asexplanatory variables. In this paper, ITC has been used only for predictorselection, after which ridge regression is employed to build the finalpredictive model. The proper leave-one-out (LOO) method of cross-validation inthis scenario is to take as holdout each of the 508 compounds before predictorthinning and compare the predicted values with the experimental data. ITC basedresults obtained here are comparable to those developed earlier.

current computer-aided drug design

Adapting the Interrelated Two-way Clustering method for Quantitative  Structure-Activity Relationship (QSAR) Modeling of a Diverse Set of Chemical  Compounds