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Toward an Optimal Approach for Variable Selection in Counter‐Propagation Neural Networks: Modeling Protein‐Tyrosine Kinase Inhibitory of Flavanoids Using Substituent Electronic Descriptors by Bahram Hemmateenejad ; Ahmadreza Mehdipour; Omar Deeb; Mahmood Sanchooli; Ramin Miri is a Computer Science article available to read on EtoBox.

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## Abstract Counter propagation neural network (CPNN) is one of the attractive tools of classification in QSAR studies. A major obstacle in classification by CPNN is finding the best subset of variables. In this study, the performance of some different feature selection algorithms including F score‐based ranking, eigenvalue ranking of PCs obtained from data set, Non‐Error‐Rate (__NER__) ranking of both descriptors and PCs, and 3‐way handling of data, Parallel Factor Analysis (PARAFAC), was evaluated in order to find the best classification model. The methods were applied for modeling protein‐tyrosine kinase inhibitory of some flavonoid derivatives using substituent electronic descriptors (__SED__) as novel source of electronic descriptors. The results showed that the best performance was achieved by __F__‐score ranking while the NER ranking of principal components (PCs) showed very fluctuate results and the worst performance was belonging to PARAFAC‐CPNN. Furthermore, comparison of results of these nonlinear algorithms with linear discriminate analysis method revealed better predictions by the former.

Who reads Toward an Optimal Approach for Variable Selection in Counter‐Propagation Neural Networks: Modeling Protein‐Tyrosine Kinase Inhibitory of Flavanoids Using Substituent Electronic Descriptors?

Author

Bahram Hemmateenejad ; Ahmadreza Mehdipour; Omar Deeb; Mahmood Sanchooli; Ramin Miri

Publisher

Wiley (John Wiley & Sons); Wiley - VCH Verlag GmbH & CO. KGaA; Wiley (ISSN 1868-1743)

Published

2011

Language

EN

Field

Computer Science (Physical Sciences)