MAIN


©1996-2014 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the  before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or  re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking: To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


OJBTM

 Online Journal of Bioinformatics © 

  Volume 15 (1): 141-156, 2014


Mining quantitative associations in peptide sequences of mosquito borne flavivirus

 

Priyanka Rajput and Dr. Usha Chouhan

 

Department of Bioinformatics.Manit,Bhopal,India

 

ABSTRACT

 

Rajput P, Chouhan U., Mining quantitative associations in peptide sequences of mosquito borne flavivirus, Onl J Bioinform., 15 (1): 141-156, 2014. Flavivirus mosquito vector causes Japanese, Murray Valley, St Louis encephalitis and West Nile and Ilheus virus disease. Knowledge of the relationships between amino acids and other parameters in molecular sequences of this virus may assist in control of the diseases. A model for mining quantitative association patterns in the amino acid sequence of flavivirus is described. Sequences were retrieved from NCBI but due to the enormous amount of data a quantitative approach was used to generate association relationships for 5 sub-families of the mosquito. The results generated were analyzed for similarities and differences in association in the amino-acids.  Association rules were generated for redundant and non-redundant protein sequences using frequent and un-frequent patterns.

 

Key words:-dataset, item set, Threshold, Support, Confidence, Pattern , quantitative association mining.


MAIN

 

FULL-TEXT(SUBSCRIBERS)