Using our techniques for extracting approximate non-tandem repeats[1] on well constructed maximal models, we derive an algorithm to find common motifs of length P that occur in N sequences with at most D differences under the Edit distance metric. We compare the effectiveness of our algorithm with the more involved algorithm of Sagot[17] for Edit distance on some real sequences. Her method has not been implemented before for Edit distance but only for Hamming distance[12],[20]. Our resulting method turns out to be simpler and more efficient theoretically and also in practice for moderately large P and D
The edit distance (a.k.a. the Levenshtein distance) between two words is defined as the minimum numb...
Pevzner and Sze [14] have introduced the Planted (l,d)-Motif Problem to find similar patterns (motif...
AbstractFinding motifs in biological sequences is one of the most intriguing problems for string alg...
Motif search is an important step in extracting meaningful patterns from biological data. Since the ...
Motivation: Motif identification for sequences has many important applications in biological studies...
The Levenshtein distance is an important tool for the comparison of symbolic sequences, with many ap...
The problem of multiple global comparison in families of biological sequences has been well-studied....
In many time series data mining problems, the analysis can be reduced to frequent pattern mining. Sp...
SUMMARY: Finding motifs in biological sequences is one of the most intriguing problems for string al...
We present in this paper three algorithms. The first extracts repeated motifs from a weighted seque...
The computation of statistical indexes such as frequency counts, expected probabilities, and over/un...
Finding motifs in biological sequences is one of the most intriguing problems for string algorithm d...
Background: The discovery of surprisingly frequent patterns is of paramount interest in bioinformati...
Abstract. Discovering approximately recurrent motifs (ARMs) in time-series is an active area of rese...
Abstract. Many algorithms for motif finding that are commonly used in bioinformatics start by sampli...
The edit distance (a.k.a. the Levenshtein distance) between two words is defined as the minimum numb...
Pevzner and Sze [14] have introduced the Planted (l,d)-Motif Problem to find similar patterns (motif...
AbstractFinding motifs in biological sequences is one of the most intriguing problems for string alg...
Motif search is an important step in extracting meaningful patterns from biological data. Since the ...
Motivation: Motif identification for sequences has many important applications in biological studies...
The Levenshtein distance is an important tool for the comparison of symbolic sequences, with many ap...
The problem of multiple global comparison in families of biological sequences has been well-studied....
In many time series data mining problems, the analysis can be reduced to frequent pattern mining. Sp...
SUMMARY: Finding motifs in biological sequences is one of the most intriguing problems for string al...
We present in this paper three algorithms. The first extracts repeated motifs from a weighted seque...
The computation of statistical indexes such as frequency counts, expected probabilities, and over/un...
Finding motifs in biological sequences is one of the most intriguing problems for string algorithm d...
Background: The discovery of surprisingly frequent patterns is of paramount interest in bioinformati...
Abstract. Discovering approximately recurrent motifs (ARMs) in time-series is an active area of rese...
Abstract. Many algorithms for motif finding that are commonly used in bioinformatics start by sampli...
The edit distance (a.k.a. the Levenshtein distance) between two words is defined as the minimum numb...
Pevzner and Sze [14] have introduced the Planted (l,d)-Motif Problem to find similar patterns (motif...
AbstractFinding motifs in biological sequences is one of the most intriguing problems for string alg...