Nonlinear Markov networks for continuous variables
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Convex optimization techniques for fitting sparse Gaussian graphical models
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
The Journal of Machine Learning Research
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Enumerating the decomposable neighbors of a decomposable graph under a simple perturbation scheme
Computational Statistics & Data Analysis
An improved shrinkage estimator to infer regulatory networks with Gaussian graphical models
Proceedings of the 2009 ACM symposium on Applied Computing
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Sparse Gaussian graphical models with unknown block structure
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence
Using modified Lasso regression to learn large undirected graphs in a probabilistic framework
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Efficient Markov network structure discovery using independence tests
Journal of Artificial Intelligence Research
The Journal of Machine Learning Research
Learning Gaussian graphical models of gene networks with false discovery rate control
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Sparse seemingly unrelated regression modelling: Applications in finance and econometrics
Computational Statistics & Data Analysis
Target detection via network filtering
IEEE Transactions on Information Theory
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
The Journal of Machine Learning Research
Gibbs ensembles for nearly compatible and incompatible conditional models
Computational Statistics & Data Analysis
Adaptive First-Order Methods for General Sparse Inverse Covariance Selection
SIAM Journal on Matrix Analysis and Applications
GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from Gene Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring gene interaction networks from ISH images via kernelized graphical models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
FeaFiner: biomarker identification from medical data through feature generalization and selection
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating building simulation parameters via Bayesian structure learning
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
A comparative study of covariance selection models for the inference of gene regulatory networks
Journal of Biomedical Informatics
Sensitivity to hyperprior parameters in Gaussian Bayesian networks
Journal of Multivariate Analysis
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We discuss the theoretical structure and constructive methodology for large-scale graphical models, motivated by their potential in evaluating and aiding the exploration of patterns of association in gene expression data. The theoretical discussion covers basic ideas and connections between Gaussian graphical models, dependency networks and specific classes of directed acyclic graphs we refer to as compositional networks. We describe a constructive approach to generating interesting graphical models for very high-dimensional distributions that builds on the relationships between these various stylized graphical representations. Issues of consistency of models and priors across dimension are key. The resulting methods are of value in evaluating patterns of association in large-scale gene expression data with a view to generating biological insights about genes related to a known molecular pathway or set of specified genes. Some initial examples relate to the estrogen receptor pathway in breast cancer, and the Rb-E2F cell proliferation control pathway.