Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Spelling Approximate Repeated or Common Motifs Using a Suffix Tree
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
Applied Combinatorics on Words (Encyclopedia of Mathematics and its Applications)
Applied Combinatorics on Words (Encyclopedia of Mathematics and its Applications)
Fast and Practical Algorithms for Planted (l, d) Motif Search
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An efficient algorithm for planted structured motif extraction
Proceedings of the 1st ACM workshop on Breaking frontiers of computational biology
Suffix tree characterization of maximal motifs in biological sequences
Theoretical Computer Science
Automated extraction of extended structured motifs using multi-objective genetic algorithm
Expert Systems with Applications: An International Journal
Motif discovery using multi-objective genetic algorithm in biosequences
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A frequent pattern mining method for finding planted (l, d)-motifs of unknown length
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
An Improved Heuristic Algorithm for Finding Motif Signals in DNA Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Component-based matching for multiple interacting RNA sequences
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
An improved voting algorithm for planted (l,d) motif search
Information Sciences: an International Journal
MoTeX: A word-based HPC tool for MoTif eXtraction
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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We present in this paper an exact algorithm for motif extraction. Efficiency is achieved by means of an improvement in the algorithm and data structures that applies to the whole class of motif inference algorithms based on suffix trees. An average case complexity analysis shows a gain over the best known exact algorithm for motif extraction. A full implementation was developed and made available online. Experimental results show that the proposed algorithm is more than two times faster than the best known exact algorithm for motif extraction.