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Computational methods [1], [2] are essential components of any motif discovery approach, but the general computational motif discovery problem remains unsolved.
We study a variation on the original formulation of the motif discovery problem, which was introduced by Yoseph et al. [12].
In this paper, we propose a new algorithm, APMotif, to solve motif discovery problem.
Motivation: The motif discovery problem consists of finding over-represented patterns in a collection of biosequences.
This simplified version of the problem is called Frequent (Structured) Motif Discovery problem [ 26].
Motivation: The motif discovery problem consists of finding recurring patterns of short strings in a set of nucleotide sequences.
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In current implementations, SISMA comes in two versions, which will be referred to as SISMA_ Smile and SISMA_ Speller, solving the structured and the frequent motif discovery problems, respectively.
It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies.
Motif discovery is the problem of finding approximately repeated patterns in unaligned sequence data.
While benchmarking studies have been done for the related problem of motif discovery [4] [6], we are not aware of any attempts at creating a benchmark for the motif search problem.
The stochastic algorithms are essentially methods which pose motif discovery as a probabilistic learning problem.
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