|
Title
|
|
A model for the evaluation of domain based classification of GPCR
|
|
Authors
|
Tannu Kumari*, Bhaskar Pant, Kamalraj R. Pardasani | |
|
Affiliation
|
Department of Mathematics, MANIT, Bhopal - 462051, India | |
|
|
||
|
Article Type
|
Hypothesis | |
|
Date
|
Received July 08, 2009; Revised July 30, 2009; Accepted September 11, 2009; Published October 11, 2009 | |
|
Abstract |
G-Protein Coupled Receptors (GPCR) are the largest family of membrane bound receptor and plays a vital role in various biological processes with their amenability to drug intervention. They are the spotlight for the pharmaceutical industry. Experimental methods are both time consuming and expensive so there is need to develop a computational approach for classification to expedite the drug discovery process. In the present study domain based classification model has been developed by employing and evaluating various machine learning approaches like Bagging, J48, Bayes net, and Naive Bayes. Various softwares are available for predicting domains. The result and accuracy of output for the same input varies for these software’s. Thus, there is dilemma in choosing any one of it. To address this problem, a simulation model has been developed using well known five softwares for domain prediction to explore the best predicted result with maximum accuracy. The classifier is developed for classification up to 3 levels for class A. An accuracy of 98.59% by Naïve Bayes for level I, 92.07% by J48 for level II and 82.14% by Bagging for level III has been achieved.
| |
|
Keywords |
GPCR; model; membrane proteins; Bayes model | |
|
Citation
|
Kumari et al., Bioinformation 4(4): 138-142 (2009) | |
|
Edited by
|
P. Kangueane
| |
|
ISSN
|
0973-2063
| |
|
Publisher
|
||
|
License
|
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |