The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Detecting non-adjoining correlations with signals in DNA
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Estimating dependency structure as a hidden variable
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A tutorial on learning with Bayesian networks
Learning in graphical models
Learning Bayesian networks with local structure
Learning in graphical models
Finding motifs using random projections
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
From promoter sequence to expression: a probabilistic framework
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
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Finding short DNA motifs using permuted markov models
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
A Uniform Projection Method for Motif Discovery in DNA Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Self-organizing neural networks to support the discovery of DNA-binding motifs
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A new approach to the assessment of the quality of predictions of transcription factor binding sites
Journal of Biomedical Informatics
DNA Motif Representation with Nucleotide Dependency
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Intelligent Data Analysis - New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
Intelligent Data Analysis - New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
Intelligent Data Analysis - New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
Bayesian unsupervised learning of DNA regulatory binding regions
Advances in Artificial Intelligence
A feature-based approach to modeling protein-DNA interactions
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Nucleosome occupancy information improves de novo motif discovery
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Improved pattern-driven algorithms for motif finding in DNA sequences
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Efficient learning of Bayesian network classifiers: an extension to the TAN classifier
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Predicting transcription factor binding sites using structural knowledge
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Fusion of domain knowledge for dynamic learning in transcriptional networks
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Prioritizing Disease Genes and Understanding Disease Pathways
International Journal of Knowledge Discovery in Bioinformatics
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The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and characterizing the binding sites of DNA-binding proteins, such as transcription factors. A common representation of transcription factor binding sites is a position specific score matrix (PSSM). This representation makes the strong assumption that binding site positions are independent of each other. In this work, we explore Bayesian network representations of binding sites that provide different tradeoffs between complexity (number of parameters) and the richness of dependencies between positions. We develop the formal machinery for learning such models from data and for estimating the statistical significance of putative binding sites. We then evaluate the ramifications of these richer representations in characterizing binding site motifs and predicting their genomic locations. We show that these richer representations improve over the PSSM model in both tasks.