Dimensionality of big data sets explored by Cluj descriptors
Dimensionality of a relatively big data set (95 compounds) observed for toxicity (mutagenicity) was explored in order to compute QSAR models. Distinct molecular descriptors were used. Dimensionality of data, using PCA, correlation plots and clustering, was evaluated. Analyzing data dimensionality al...
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| Veröffentlicht in: | Studia Universitatis Babeș-Bolyai. Chemia 2017-01, Vol.62 (3), p.197-204 |
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| container_title | Studia Universitatis Babeș-Bolyai. Chemia |
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| creator | Lungu, Claudiu Ersali, Sara Szefler, Beata Pîrvan-Moldovan, Atena Basak, Subhash Diudea, Mircea V. |
| description | Dimensionality of a relatively big data set (95 compounds) observed for toxicity (mutagenicity) was explored in order to compute QSAR models. Distinct molecular descriptors were used. Dimensionality of data, using PCA, correlation plots and clustering, was evaluated. Analyzing data dimensionality allowed model optimization. Docking studies and PCA were used in order to expand data dimensionality. Pearson correlation coefficient ([r.sup.2]) values, obtained for both perceptive and predictive models, were satisfactory. Keywords: topological descriptor, QSAR, data dimensionality, mutagenity, principal component analysis (PCA), Ames test. |
| doi_str_mv | 10.24193/subbchem.2017.3.16 |
| format | Article |
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| title | Dimensionality of big data sets explored by Cluj descriptors |
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