Title: Drug/nondrug classification with consensual Self-Organising Map and Self-Organising Global Ranking algorithms

Authors: Ayca C. Pehlivanli, Okan K. Ersoy, Turgay Ibrikci

Addresses: Computer Engineering Department, Istanbul Kultur University, Atakoy, Bakirkoy 34156, Istanbul, Turkey. ' School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. ' Department of Electrical Electronics Engineering, Cukurova University, Balcali 01330, Adana, Turkey

Abstract: In this paper, a special consensual approach is discussed for separating the druglike compounds from the non-druglike compounds. It involves a group decision to produce a consensus of multiple classification results obtained with a single classification algorithm. The individual results are obtained with either the Self Organising Global Ranking (SOGR) or Self Organising Map (SOM). The main difference between the proposed algorithm and SOM is the neighbourhood concept. The constructed consensual 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 a postprocessing unit for consensus. The confirmed drugs were classified with a consensual accuracy of 90.63% while nondrugs resulted in 80.44% accuracy. The SOGR results were better than the SOM algorithm results.

Keywords: classifier design; classifier evaluation; neural networks; consensual SOM; self-organising maps; drug design; nondrug compounds; classification; bioinformatics; self-organising global ranking; consensual SOGR.

DOI: 10.1504/IJCBDD.2008.022212

International Journal of Computational Biology and Drug Design, 2008 Vol.1 No.4, pp.434 - 445

Published online: 22 Dec 2008 *

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