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Association Rule Mining to Deduce the Most Frequently Occurring Amino Acid Patterns in HIV
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 51 - Number 9 |
| Year of Publication: 2012 |
| Authors: Kumud Pant, Bhasker Pant, Shweta Negi |
10.5120/8071-1465
|
Kumud Pant, Bhasker Pant, Shweta Negi . Association Rule Mining to Deduce the Most Frequently Occurring Amino Acid Patterns in HIV. International Journal of Computer Applications. 51, 9 ( August 2012), 29-32. DOI=10.5120/8071-1465
Abstract
HIV is one of the most dreaded diseases of the century. Throughout the world efforts are underway to develop new vaccines and design new drugs so as to combat this viral menace. In an effort to probe deeper into the functioning of these viruses we present association based rules formulation so as to decipher the most frequently occurring amino acids in these viruses. This is a novel attempt of its kind since we are attempting to find put the most informative association rules using Apriori algorithm implemented through WEKA. The information generated can be of great use to molecular biologists and drug designers since the associated amino acids can be a very good drug targets. Our findings suggest that L-Selenocysteine and L-Pyrrolysine are most frequently associated amino acids in the 4 classes of virulent proteins analyzed for association rules and Cyteine and Arginine show the strongest association in one of the class analyzed i. e. Gp41. Hence these can be potential drug candidates.
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