Introduction and background Graphical models [1,2] are a class of statistical models which combine the rigour of a probabilistic approach with the intuitive representation of relationships given by graphs. It can be summarised as follows: 1. Open in a separate window. Simulation results We tested the proposed approach on synthetic data sets using three established performance measures: sensitivity , specificity and accuracy. Its true structure is composed by 27 nodes and 52 edges of possible edges , and its probability distribution has parameters.
IAMB was used to learn the Markov blanket of each node as a preliminary step to reduce the number of its candidate parents and children; a network structure satisfying these constraints is then identified as in the Grow—Shrink algorithm . The equivalent sample size was set to This is the same approach detailed in Friedman et al. The conditional independence test used in MMPC and the score functions used in HC are the ones illustrated in the previous points.
The performance measures were estimated for each combination of network, sample size and structure learning algorithm as follows: 1. Since results are very similar, they will be discussed together;. Significant edges were then used to build an averaged network structure;. Applications to molecular expression profiles In order to demonstrate the effectiveness of the proposed approach on experimental data sets, we will examine two gene expression data sets from Nagarajan et al.
Protein signalling in flow cytometry data In a landmark study, Sachs et al.
Restricted graphical log-linear models
Conclusions Graphical models and network abstractions have enjoyed considerable attention across the biological and medical communities. References 1. Koller D. Probabilistic graphical models: principles and techniques. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. Whittaker J. Graphical models in applied multivariate statistics. Edwards D. Introduction to graphical modelling. Neapolitan R. Learning Bayesian networks. Korb K. Bayesian artificial intelligence. Heckerman D.
Introduction to Graphical Modelling
Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning. Geiger D, Heckerman D. Learning Gaussian networks, Tech. Bromberg F. Efficient Markov network structure discovery using independence tests. Journal of Artificial Intelligence Research. Castelo R. A robust procedure for Gaussian graphical model search from microarray data with p larger than n. Journal of Machine Learning Research.
Friedman N. In: Laskey K. Learning Bayesian networks by genetic algorithms: a case study in the prediction of survival in malignant skin melanoma. In: Keravnou E. Tsamardinos I. Elidan G. Bayesian network repository. Murphy P.
UCI machine learning repository. Data analysis with Bayesian networks: a bootstrap approach. Proceedings of the 15th annual conference on uncertainty in artificial intelligence UAI Morgan Kaufmann; Efron B. An introduction to the bootstrap. Claeskens G.
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Model selection and model averaging. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Nagarajan R. Functional relationships between genes associated with differentiation potential of aged myogenic progenitors. Frontiers in Physiology. Sachs K. Causal protein-signaling networks derived from multiparameter single-cell data. Lauritzen S. Graphical models. Chickering D. Optimal structure identification with greedy search. DeGroot M. Probability and statistics.
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Graphical model - Wikipedia
Adaptive probabilistic networks with hidden variables. Algorithms for large scale Markov blanket discovery. In: Russell I. Proceedings of the 16th international Florida artificial intelligence research society conference. AAAI Press; To purchase short term access, please sign in to your Oxford Academic account above. Don't already have an Oxford Academic account? Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.
Sign In or Create an Account. Sign In. Advanced Search. Article Navigation. Close mobile search navigation Article Navigation. Volume Elliptical graphical modelling D. Oxford Academic. Google Scholar. Cite Citation. Permissions Icon Permissions. Probabilistic Graphical Models. Massachusetts: MIT Press. Scandinavian Journal of Statistics. Annals of Statistics. Outline Index. Descriptive statistics. Mean arithmetic geometric harmonic Median Mode.
Central limit theorem Moments Skewness Kurtosis L-moments. Index of dispersion. Grouped data Frequency distribution Contingency table. Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Scatter plot. Data collection.
Sampling stratified cluster Standard error Opinion poll Questionnaire.
Scientific control Randomized experiment Randomized controlled trial Random assignment Blocking Interaction Factorial experiment. Adaptive clinical trial Up-and-Down Designs Stochastic approximation. Cross-sectional study Cohort study Natural experiment Quasi-experiment. Statistical inference. Z -test normal Student's t -test F -test. Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator.
Correlation Regression analysis. Pearson product-moment Partial correlation Confounding variable Coefficient of determination.
- Identifying significant edges in graphical models of molecular networks?
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Simple linear regression Ordinary least squares General linear model Bayesian regression.