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This problem is different from the motif evaluation problem, since an item occurs only once per itemset, which is not the case of motifs, where an item (called symbol) may occur repeatedly.
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Specifically, we ran DME [11] on both the conservation-free and the combined-conservation sets and recorded p-values for DME-identified motifs, reporting motifs with p<0.05 (see Motif evaluation and discovery in Materials and methods).
The top three predicted motifs with p-value<0.05 (see Motif evaluation and discovery in Materials and methods) for each TF are given in Table S2; only two significant motifs were identified for NME2 and EP300.
Motif evaluation in terms of ranking then becomes a motif-to-motif similarity task.
This proves that, in general, this measure is not suitable for motif evaluation.
The ineffectiveness of the motif evaluation metric used can be one of the reasons behind this.
Hence, a complexity score-based filtering [ 17] has to be used in candidate motif evaluation.
The CSP, also known as Motif Finding problem, has applications in Coding Theory and Computational Biology.
Computational methods [1], [2] are essential components of any motif discovery approach, but the general computational motif discovery problem remains unsolved.
The motif finding problem can be stated simply as follows.
In the structured motif extraction problem, the component motifs M i are unknown before the extraction.
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