Detecting non-adjoining correlations with signals in DNA
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Finding motifs in the twilight zone
Proceedings of the sixth annual international conference on Computational 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
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Efficient local protein structure prediction
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
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In this paper, we describe a novel method called Secondary Verification which assesses the quality of predictions of transcription factor binding sites. This method incorporates a distribution of prediction scores over positive examples (i.e. the actual binding sites) and is shown to be superior to p-value, routinely used statistical significance assessment, which uses only a distribution of prediction scores over background sequences. We also discuss how to integrate both distributions into a framework called Secondary Verification Assessment method which evaluates the quality of a model of a transcription factor. Based on that we create a hybrid representation of a transcription factor: we select the description (with or without dependencies) which is best for the transcription factor considered.