Prediction of aromatase inhibitory activity using the efficient linear method (ELM)

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dc.contributor.authorShoombuatong, Watshara
dc.contributor.authorPrachayasittikul, Veda
dc.contributor.authorPrachayasittikul, Virapong
dc.contributor.authorNantasenamat, Chanin
dc.date.accessioned2015-04-22T12:28:07Z
dc.date.available2015-04-22T12:28:07Z
dc.date.issued2015-03-20
dc.description.abstractAromatase inhibition is an effective treatment strategy for breast cancer. Currently, several in silico methods have been developed for the prediction of aromatase inhibitors (AIs) using artificial neural network (ANN) or support vector machine (SVM). In spite of this, there are ample opportunities for further improvements by developing a simple and interpretable quantitative structure-activity relationship (QSAR) method. Herein, an efficient linear method (ELM) is proposed for constructing a highly predictive QSAR model containing a spontaneous feature importance estimator. Briefly, ELM is a linear-based model with optimal parameters derived from genetic algorithm. Results showed that the simple ELM method displayed robust performance with 10-fold cross-validation MCC values of 0.64 and 0.56 for steroidal and non-steroidal AIs, respectively. Comparative analyses with other machine learning methods (i.e. ANN, SVM and decision tree) were also performed. A thorough analysis of informative molecular descriptors for both steroidal and non-steroidal AIs provided insights into the mechanism of action of compounds. Our findings suggest that the shape and polarizability of compounds may govern the inhibitory activity of both steroidal and non-steroidal types whereas the terminal primary C(sp3) functional group and electronegativity may be required for non-steroidal AIs. The R code of the ELM method is available at http://dx.doi.org/10.6084/m9.figshare.1274030.en
dc.identifier.issn1611-2156
dc.identifier.urihttp://hdl.handle.net/2003/34047
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-7300
dc.language.isoen
dc.relation.ispartofseriesEXCLI Journal ; Vol. 14, 2015en
dc.subjectefficient linear methoden
dc.subjectgenetic algorithmen
dc.subjectaromataseen
dc.subjectaromatase inhibitorsen
dc.subjectQSARen
dc.subjectdata miningen
dc.subject.ddc610
dc.titlePrediction of aromatase inhibitory activity using the efficient linear method (ELM)en
dc.typeText
dc.type.publicationtypearticle
dcterms.accessRightsopen access
eldorado.dnb.zdberstkatid2132560-1

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