Authors: Ayca C. Pehlivanli, Turgay Ibrikci, Okan K. Ersoy
Addresses: Computer Engineering Department, Istanbul Kultur University, Atakoy, Istanbul, Bakirkoy, 34156, Turkey. ' Department of Electrical Electronics Engineering, Cukurova University, Balcali, 01330, Adana, Turkey. ' School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Abstract: A special consensual approach is discussed for separating a molecular group with a proven pharmacological activity from another molecular group without any activity. It is mainly a group decision to produce a consensus of multiple classification results obtained with a single classification algorithm. For this purpose, the constructed model has a preprocessing unit which consists of transformation of input patterns by random matrices and median filtering to generate independent errors for a single type of classifier and postprocessing for consensus. The neural network based consensus classifier operating with MOE descriptors was applied to a set of 641 chemical structures. The confirmed drugs were classified with an accuracy of 86.54% while nondrugs resulted in 82.67% accuracy.
Keywords: classifier design; classifier evaluation; neural networks; drug design; nondrug compounds; classification; molecular groups; input patterns; random matrices; median filtering; postprocessing; consensus.
International Journal of Computational Biology and Drug Design, 2008 Vol.1 No.3, pp.224 - 234
Available online: 26 Nov 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article