LEDA: a platform for combinatorial and geometric computing
Communications of the ACM
Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Finding motifs in the twilight zone
Proceedings of the sixth annual international conference on Computational biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Spelling Approximate Repeated or Common Motifs Using a Suffix Tree
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
An efficient algorithm for planted structured motif extraction
Proceedings of the 1st ACM workshop on Breaking frontiers of computational biology
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Automatically identifying frequent composite patterns in DNA sequences is an important task in bioinformatics, especially when all the basic elements (or monad patterns) of a composite pattern are weak. In this paper, we compare one straightforward approach to assemble the monad patterns into composite patterns to two other rather complex approaches. Both our theoretical analysis and empirical results show that this overlooked straightforward method can be several orders of magnitude faster. Furthermore, different from the previous understandings, the empirical results show that the runtime superiority among the three approaches is closely related to the insignificance of the monad patterns.