Abstract
Interrelated Two-way Clustering (ITC) is an unsupervised clustering method developed to divide samples into two groups in gene expression data obtained through microarrays, selecting important genes simultaneously in the process. This has been found to be a better approach than conventional clustering methods like K-means or selforganizing map for the scenarios when number of samples is much smaller than number of variables (n«p). In this paper we used the ITC approach for classification of a diverse set of 508 chemicals regarding mutagenicity. A large number of topological indices (TIs), 3-dimensional, and quantum chemical descriptors, as well as atom pairs (APs) has been used as explanatory variables. In this paper, ITC has been used only for predictor selection, after which ridge regression is employed to build the final predictive model. The proper leave-one-out (LOO) method of cross-validation in this scenario is to take as holdout each of the 508 compounds before predictor thinning and compare the predicted values with the experimental data. ITC based results obtained here are comparable to those developed earlier.
Keywords: Atom pairs, interrelated two-way clustering, mutagenicity, quantum chemical descriptors, ridge regression, topological indices.
Current Computer-Aided Drug Design
Title:Adapting Interrelated Two-Way Clustering Method for Quantitative Structure-Activity Relationship (QSAR) Modeling of Mutagenicity/Non- Mutagenicity of a Diverse Set of Chemicals
Volume: 9 Issue: 4
Author(s): Subhabrata Majumdar, Subhash C. Basak and Gregory D. Grunwald
Affiliation:
Keywords: Atom pairs, interrelated two-way clustering, mutagenicity, quantum chemical descriptors, ridge regression, topological indices.
Abstract: Interrelated Two-way Clustering (ITC) is an unsupervised clustering method developed to divide samples into two groups in gene expression data obtained through microarrays, selecting important genes simultaneously in the process. This has been found to be a better approach than conventional clustering methods like K-means or selforganizing map for the scenarios when number of samples is much smaller than number of variables (n«p). In this paper we used the ITC approach for classification of a diverse set of 508 chemicals regarding mutagenicity. A large number of topological indices (TIs), 3-dimensional, and quantum chemical descriptors, as well as atom pairs (APs) has been used as explanatory variables. In this paper, ITC has been used only for predictor selection, after which ridge regression is employed to build the final predictive model. The proper leave-one-out (LOO) method of cross-validation in this scenario is to take as holdout each of the 508 compounds before predictor thinning and compare the predicted values with the experimental data. ITC based results obtained here are comparable to those developed earlier.
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Cite this article as:
Majumdar Subhabrata, Basak C. Subhash and Grunwald D. Gregory, Adapting Interrelated Two-Way Clustering Method for Quantitative Structure-Activity Relationship (QSAR) Modeling of Mutagenicity/Non- Mutagenicity of a Diverse Set of Chemicals, Current Computer-Aided Drug Design 2013; 9 (4) . https://dx.doi.org/10.2174/15734099113096660045
DOI https://dx.doi.org/10.2174/15734099113096660045 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
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