Drug/nondrug classification with consensual Self-Organising Map and Self-Organising Global Ranking algorithms Online publication date: Mon, 22-Dec-2008
by Ayca C. Pehlivanli, Okan K. Ersoy, Turgay Ibrikci
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 1, No. 4, 2008
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Biology and Drug Design (IJCBDD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook