Please use this identifier to cite or link to this item:
Title: Prediction of putative adverse drug reaction-related proteins from primary sequence by support vector machines
Authors: Zhi, L.J.
Lian, Y.H. 
Chan, J.Z. 
Zhi, W.C. 
Yu, Z.C. 
Issue Date: 2005
Citation: Zhi, L.J., Lian, Y.H., Chan, J.Z., Zhi, W.C., Yu, Z.C. (2005). Prediction of putative adverse drug reaction-related proteins from primary sequence by support vector machines. International Journal of Pharmaceutical Medicine 19 (5-6) : 317-322. ScholarBank@NUS Repository.
Abstract: Introduction: Adverse drug reactions (ADRs) are responsible for the failure of a significant portion of investigative drugs trials and the major reason for the withdrawal of drugs from clinical research. A number of ADRs are caused by the (undesired) interaction of drugs with key proteins involved in normal biological processes. Identification of these ADR-related proteins facilitates the design of drugs with fewer adverse effects by rationally avoiding unwanted interaction with these proteins. Method: This work explores the use of a statistical learning method, support vector machines (SVMs), for the identification of potential ADR-related proteins. A SVM classification system was trained and tested by using 759 ADR-related proteins of different species and 2280 non-ADR-related proteins. Results: 93.9% of the ADR-related proteins and 98.2% of non-ADR-related proteins were correctly classified. Discussion: The SVM is potentially useful for facilitating the identification of ADR-related proteins. The development of methods to identify ADR indications of ADR-related proteins are progressing well, an example of which is the web-based ADR-related protein prediction tool SVMDART, which can be accessed at © 2005 Adis Data Information BV. All rights reserved.
Source Title: International Journal of Pharmaceutical Medicine
ISSN: 13649027
DOI: 10.2165/00124363-200519050-00009
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Apr 19, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.