Show simple item record

dc.contributor.author Vander Heyden, Yvan
dc.contributor.author Dejaegher, Bieke
dc.contributor.author Jensen, Richard
dc.contributor.author Funar-Timofei, Simona
dc.contributor.author Goodarzi, Mohammad
dc.date.accessioned 2011-07-13T14:59:39Z
dc.date.available 2011-07-13T14:59:39Z
dc.date.issued 2011-07-13
dc.identifier.citation Vander Heyden , Y , Dejaegher , B , Jensen , R , Funar-Timofei , S & Goodarzi , M 2011 , ' Fuzzy rough set as feature selection for QSAR modeling of 2,4,5-trisubstituted imidazoles, nontoxic modulators of P-glycoprotein mediated multidrug resistance ' . en
dc.identifier.other PURE: 169165
dc.identifier.other dspace: 2160/7141
dc.identifier.uri http://hdl.handle.net/2160/7141
dc.description M. Goodarzi, S. Funar-Timofei, B. Dejaegher, R. Jensen, Y. Vander Heyden. Fuzzy rough set as feature selection for QSAR modeling of 2,4,5-trisubstituted imidazoles, nontoxic modulators of P-glycoprotein mediated multidrug resistance, 12th Scandinavian Symposium on Chemometrics (SSC12), 2011. en
dc.description.abstract In cancer chemotherapy, multidrug resistance (MDR) is a major clinical problem which occurs by an influential mechanism and which leads to the failure of cancer chemotherapy and/or a relapse of the cancer. In this study, Fuzzy Rough Set and Genetic Algorithms were compared as variable selection techniques, while both linear and nonlinear 2D QSAR models were constructed for predicting the multidrug resistance modulating potency (expressed as ED50 values) of 2,4,5-trisubstituted imidazoles, as potent and nontoxic modulators of P-glycoprotein mediated multidrug resistance. The variables to select are the proper molecular descriptors. The (linear) Multiple Linear Regression (MLR) and the nonlinear Radial Basis Function Neural Network (RBFNN) techniques were used to search for a relation between the selected descriptors and the corresponding activity. A cross-validation approach and a test set were used as internal and external model validation, respectively. The results indicate that Fuzzy Rough Set can be used as a descriptor selection technique because the obtained models have a similar predictive property compared to those where Genetic Algorithm as a common feature selection method, was applied. en
dc.language.iso eng
dc.title Fuzzy rough set as feature selection for QSAR modeling of 2,4,5-trisubstituted imidazoles, nontoxic modulators of P-glycoprotein mediated multidrug resistance en
dc.type Still image en
dc.type.publicationtype Conference poster en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

Statistics