Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
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
A tutorial on learning with Bayesian networks
Learning in graphical models
Learning Bayesian networks with local structure
Learning in graphical models
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
Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites
WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Adaptive Meta-Clustering Approach: Combining the Information from Different Clustering Results
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Clustering Binary Fingerprint Vectors with Missing Values for DNA Array Data Analysis
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Inference of transcriptional regulation relationships from gene expression data
Proceedings of the 2003 ACM symposium on Applied computing
Mining multiple phenotype structures underlying gene expression profiles
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Algorithms for clustering high dimensional and distributed data
Intelligent Data Analysis
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
A new method to mine gene regulation relationship information
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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The recent growth in genomic data and measurement of genome-wide expression patterns allows to examine gene regulation by transcription factors using computational tools. In this work, we present a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data. These models represent the joint distribution of transcription factor binding sites and of expression levels of a gene in a single model. Learning a combined probability model of binding sites and expression patterns enables us to improve the clustering of the genes based on the discovery of putative binding sites and to detect which binding sites and experiments best characterize a cluster. To learn such models from data, we introduce a new search method that rapidly learns a model according to a Bayesian score. We evaluate our method on synthetic data as well as on real data and analyze the biological insights it provides.