An enhanced algorithm for frequent pattern mining from biological sequences

Lakshmanna, K. and Kaluri, R. and Reddy, G.T. and Nagaraja, G. and Subramanian, Dhenesh V. (2016) An enhanced algorithm for frequent pattern mining from biological sequences. International Journal of Pharmacy and Technology, 8 (2). pp. 12776-12784. ISSN 0975-766X

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Abstract

Bio-data analysis deals with the most vital discovering problem of similarity search and finding relationship among bio sequences and structures. In this paper, we are trading the problem of discovering the most recurrently occurring patterns in a given DNA or protein sequence. Several on hand tools need the user to spell out gap constraints in advance in turn to find specific patterns. Practically it is not possible for the user to provide the gap constraints. So the need arises of budding an algorithm to obtain the patterns easily on its own without the need of user intervention in the form of mentioning of gap constraints. We have got two analytical methods to find out the recurrent subsequences and guesstimate the maximum support for data with complexity O(|T|.Sup) where |T| stands for text sequence length and Sup represents the number of occurrences of the pattern. We are proposing an altered version of the previously proposed algorithm with complexity O(|T|).

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User:	Dhenesh Subramanian
Date Deposited:	21 Nov 2016 22:40
Last Modified:	21 Nov 2016 22:40
URI:	https://repository.usp.ac.fj/id/eprint/9452

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