Detecting non-adjoining correlations with signals in DNA
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
REGULARIZERS FOR ESTIMATING DISTRIBUTIONS OF AMINO ACIDS FROM SMALL SAMPLES
REGULARIZERS FOR ESTIMATING DISTRIBUTIONS OF AMINO ACIDS FROM SMALL SAMPLES
A new approach to the assessment of the quality of predictions of transcription factor binding sites
Journal of Biomedical Informatics
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In this paper we explore several techniques of analysing sequence alignments. Their main idea is to generalize an alignment by means of a probability distribution. The Dirichlet mixture method is used as a reference to assess new techniques. They are compared based on a cross validation test with both synthetic and real data: we use them to identify sequence-structure relationships between target protein and possible local motifs. We show that the Beta method is almost as successful as the reference method, but it is much faster (up to 17 times). MAP (Maximum a Posteriori) estimation for two PSSMs (Position Specific Score Matrices) introduces dependencies between columns of an alignment. It is shown in our experiments to be much more successful than the reference method, but it is very computationally expensive. To this end we developed its parallel implementation.