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Predict the Frequent Pattern of Amino Acids using Apriori Algorithm, Genetic Algorithm and Fuzzy Logic

Author(s):

Nikit Patel , Parul Institutes of Engineering & Technology; Pratik Kumar, Parul Institutes of Engineering & Technology

Keywords:

Bio-Data Mining, Protein sequence, Association Rules, Genetic Algorithm & Fuzzy logic

Abstract

Data Mining is the process of extracting or mining the patterns from very large amount of biological datasets. In recent years, rapid developments in genomics and proteomics have generated a large amount of biological data. So a critical problem in biological data analysis is to classify the biological sequences and structures based on their critical features and functions. From the literature, various algorithms have been employed in generating frequent patterns for distinct application. This algorithm has been lost of frequent produce. So it’s meaningless. Here my approach is to compare the frequent pattern using two algorithms and optimise the data. So it’s very useful for us. Our approach aims at extracting the hidden and the most dominating amino acids among the infected protein sequence which causes some infections in human. We handle this problem by predicting patterns apply strong association rules along with apriori algorithm and genetic algorithm. Also apply the fuzzy logic to optimise data and interesting frequent pattern get form the protein sequence database. This Frequent Pattern is very useful to drug design, drug discovery etc

Other Details

Paper ID: IJSRDV2I3233
Published in: Volume : 2, Issue : 3
Publication Date: 01/06/2014
Page(s): 996-1000

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