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Adapting the Interrelated Two-way Clustering method for Quantitative Structure-Activity Relationship (QSAR) Modeling of a Diverse Set of Chemical Compounds | Subhabrata Majumdar
; Subhash C. Basak
; Gregory D. Grunwald
; | Date: |
31 May 2013 | 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 self-organizing map for the scenarios when number of
samples 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) have 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. | Source: | arXiv, 1305.7285 | Services: | Forum | Review | PDF | Favorites |
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