Introduction to algorithms
Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Search algorithms for biosequences using random projection
Search algorithms for biosequences using random projection
Combinatorial Designs: Constructions and Analysis
Combinatorial Designs: Constructions and Analysis
UPNT: Uniform Projection and Neighbourhood Thresholding method for motif discovery
International Journal of Bioinformatics Research and Applications
Massively Parallelized DNA Motif Search on the Reconfigurable Hardware Platform COPACOBANA
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
A two-block motif discovery method with improved accuracy
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
A Cluster Refinement Algorithm for Motif Discovery
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
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Buhler and Tompa [5] introduced the random projection algorithm for the motif discovery problem and demonstrated that this algorithm performs well on both simulated and biological samples. We describe a modification of the random projection algorithm, called the uniform projection algorithm, which utilizes a different choice of projections. We replace the random selection of projections by a greedy heuristic that approximately equalizes the coverage of the projections. We show that this change in selection of projections leads to improved performance on motif discovery problems. Furthermore, the uniform projection algorithm is directly applicable to other problems where the random projection algorithm has been used, including comparison of protein sequence databases.