Finding motifs using random projections
RECOMB '01 Proceedings of the fifth 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 the Extended (l, d)-Motif Problem with Unknown Number of Binding Sites
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
A generic motif discovery algorithm for sequential data
Bioinformatics
An Exact Data Mining Method for Finding Center Strings and All Their Instances
IEEE Transactions on Knowledge and Data Engineering
Fast and Practical Algorithms for Planted (l, d) Motif Search
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RISOTTO: fast extraction of motifs with mismatches
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
Trie-based apriori motif discovery approach
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
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Identification and characterization of gene regulatory binding motifs is one of the fundamental tasks toward systematically understanding the molecular mechanisms of transcriptional regulation. Recently, the problem has been abstracted as the challenge planted (l, d)- motif problem. Previous studies have developed numerous methods to solve the problem. But most of methods need to specify the length l of a motif in advance. In this study, we present an exact and efficient algorithm, called Apriori-Motif, without given l. The algorithm uses breadth first search and prunes the search space quickly by the downward closure property used in Apriori, a classical algorithm of frequent pattern mining. Empirical study shows that Apriori-Motif is better than some existing methods.