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Inspired by the methodology of three-way decisions and protein tri-partition, this paper proposes a frequent pattern discovery algorithm for a new type of pattern by dividing the alphabet into strong, medium, and weak parts.
Given that dynamic means a non-stationary context, the pattern discovery algorithm must adapt to the available data streams.
We used SPLASH [10], [36], a deterministic pattern discovery algorithm, to identify patterns across species after masking repeats and coding exons.
Brazma et al. introduced the Pattern Discovery Algorithm, which realizes an exhaustive search for three different classes of motifs.
In contrast to the Pattern Discovery Algorithm, our approach uses the generalized suffix tree annotated with the letters of the sequence alphabet.
We selected those genes most highly induced by LasR alone or by RhlR alone and subjected the presumed upstream regulatory sequences to the pattern discovery algorithm CONSENSUS [ 28].
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Parallel frequent pattern discovery algorithms exploit parallel and distributed computing resources to relieve the sequential bottlenecks of current frequent pattern mining (FPM) algorithms.
Existing software solutions which offer pattern discovery algorithms for the correct identification and downstream analysis of microsatellites are scarce and are proving to be inefficient to analyze large, exponentially increasing, sequenced genomes.
Even though a number of algorithms have been developed to efficiently parallelize frequent pattern discovery algorithms that are based on the candidate-generation-and-counting framework, the problem of parallelizing the more efficient projection-based algorithms has received relatively little attention and existing parallel formulations have been targeted only toward shared-memory architectures.
Pattern discovery algorithms search for recurrent motifs within sequences.
Many pattern discovery algorithms (see, e.g., [ 21] and references therein) assume that the errors are randomly distributed in the pattern.
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