AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: CLAUDIU LUNGU, SARA ERSALI, BEATA SZEFLER, ATENA PÎRVAN-MOLDOVAN, SUBHASH BASAK, MIRCEA V. DIUDEA.
DOI: https://doi.org/10.24193/subbchem.2017.3.16
Published Online: 2017-09-30
Published Print: 2017-09-30
VIEW PDF: 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 allowed model optimization. Docking studies and PCA were used in order to expand data dimensionality. Pearson correlation coefficient (r2) values, obtained for both perceptive and predictive models, were satisfactory.
Keywords: topological descriptor, QSAR, data dimensionality, mutagenity, principal component analysis (PCA), Ames test.
