Mining Association Rules in Dengue Gene Sequence with Latent Periodicity | Zendy

Marimuthu Thangam | Zendy; Balamurugan Vanniappan | Zendy

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Mining Association Rules in Dengue Gene Sequence with Latent Periodicity

Author(s) -

Marimuthu Thangam,

Balamurugan Vanniappan

Publication year - 2015

Publication title -

computational biology journal

Language(s) - English

Resource type - Journals

eISSN - 2314-4173

pISSN - 2314-4165

DOI - 10.1155/2015/839692

Subject(s) - dengue fever , dengue virus , suffix , suffix tree , sequence (biology) , association rule learning , computer science , tree (set theory) , serotype , computational biology , artificial intelligence , data mining , virology , algorithm , biology , mathematics , genetics , combinatorics , data structure , philosophy , linguistics , programming language

The mining of periodic patterns in dengue database is an interesting research problem that can be used for predicting the future evolution of dengue viruses. In this paper, we propose an algorithm called Recurrence Finder (RECFIN) that uses the suffix tree for detecting the periodic patterns of dengue gene sequence. Also, the RECFIN finds the presence of palindrome which indicates the possibilities of formation of proteins. Further, this paper computes the periodicity of nucleic acid and amino acid sequences of any length. The periodicity based association rules are used to diagnose the type of dengue. The time complexity of the proposed algorithm is O(n2). We demonstrate the effectiveness of the proposed approach by comparing the experimental results performed on dengue virus serotypes dataset with NCBI-BLAST algorithm

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