A Frequent Pattern Mining Method for Finding Planted Motifs of Unknown Length in DNA Sequences
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 them need to specify the length l of a planted motif in advance and use depth first search strategy. In this study, we present an exact and efficient algorithm, called Apriori-Motif, without given the length l of a planted motif a priori. And a breadth first search strategy is used to prune search space quickly by the downward closure property utilized in Apriori, which is a classical algorithm for frequent pattern mining. Empirical study shows that Apriori-Motif is better than some existing methods.
frequent pattern motif apriori downward closure property