FREQUENT PERIODIC CRYPTIC SEQUENCE MINING IN BIOLOGICAL DATA

Amino acid sequences are known to constantly mutate and diverge unless there is a limiting condition that makes such a change deleterious. The few existing algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other applications. In this paper, we present a new algorithm called Constraint Based Frequent Motif Mining (CBFMM). CBFMM is a flexible Frequent Pattern-tree-based algorithm that can be used to find frequent patterns with a variety of definitions of motif (pattern) models. They can play an active role in protein and nucleotide pattern mining, which ensure in identification of potentiating malfunction and disease. Therefore, insights into any aspect of the repeats – be it structure, function or evolution – would prove to be of some importance. This study aims to address the relationship between protein sequence and its threedimensional structure, by examining if large cryptic sequence repeats have the same structure. We have tested the proposed algorithm on biological domains. The conducted comparative study demonstrates the applicability and effectiveness of the proposed algorithm.