A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, time series data. The motifs uni-variate a well studied problem and, while being relatively new area research, there are also several proposals for multi-variate motif discovery. Unfortunately, search among multiple variates an expensive process, as potential number sub-spaces whi...

Most of existing methods for DNA motif discovery consider only a single set of sequences to find an over-represented motif. In contrast, we consider multiple sets of sequences where we group sets associated with the same motif into a cluster, assuming that each set involves a single motif. Clustering sets of sequences yields clusters of coherent motifs, improving signal-to-noise ratio or enabli...

MOTIVATION The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches. RESULTS We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biologica...

This project investigates a number of algorithms for discovering regulatory motifs in the DNA of magnetic bacteria. The most promising of these is then implemented in Java and evaluated using a combination of synthetic data and previously characterised real-world data. The implemented algorithm is also used to study three previously uncharacterised datasets; a number of new potential motifs are...

Methods for the identification of transcription factor binding sites have proved to be useful for deciphering genetic regulatory networks. The strengths and weaknesses for a number of available web tools are not fully understood. Here, we designed a comprehensive set of performance measures and benchmarked sequence-based motif discovery tools using large scale datasets (derived from Escherichia...

Discovering approximately recurrent motifs (ARMs) in timeseries is an active area of research in data mining. Exact motif discovery was later defined as the problem of efficiently finding the most similar pairs of timeseries subsequences and can be used as a basis for discovering ARMs. The most efficient algorithm for solving this problem is the MK algorithm which was designed to find a single ...

Finding common patterns, motifs, in a set of DNA sequences is an important problem in bioinformatics. One common representation of motifs is a string with symbols A, C, G, T and N where N stands for the wildcard symbol. In this paper, we introduce a more general motif discovery problem without any weaknesses of the Planted (l,d)-Motif Problem and also a set of control sequences as an additional...

Motif discovery is a general and important problem in bioinformatics, as motifs often are used to infer biologically important sites in bio-molecular sequences. Many problems in bioinformatics are naturally cast in terms of sequences, and distance measures for sequences derived from edit distance is fundamental in bioinformatics. Geometric Crossover is a representation-independent definition of...

Motif discovery is a general and important problem in bioinformatics, as motifs often are used to infer biologically important sites in bio-molecular sequences. Many problems in bioinformatics are naturally cast in terms of sequences, and distance measures for sequences derived from edit distance is fundamental in bioinformatics. Geometric Crossover is a representation-independent definition of...