Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning bayesian networks from data
Learning bayesian networks from data
Extensions to gene set enrichment
Bioinformatics
Learning Factor Graphs in Polynomial Time and Sample Complexity
The Journal of Machine Learning Research
Analyzing gene expression data in terms of gene sets
Bioinformatics
Bioinformatics
Genomic Signal Processing (Princeton Series in Applied Mathematics)
Genomic Signal Processing (Princeton Series in Applied Mathematics)
| Hi-index | 0.00 |
Bayesian network models are commonly used to model gene expression data. Some applications require a comparison of the network structure of a set of genes between varying phenotypes. In principle, separately fit models can be directly compared, but it is difficult to assign statistical significance to any observed differences. There would therefore be an advantage to the development of a rigorous hypothesis test for homogeneity of network structure. In this paper, a generalized likelihood ratio test based on Bayesian network models is developed, with significance level estimated using permutation replications. In order to be computationally feasible, a number of algorithms are introduced. First, a method for approximating multivariate distributions due to Chow and Liu (1968) is adapted, permitting the polynomial-time calculation of a maximum likelihood Bayesian network with maximum indegree of one. Second, sequential testing principles are applied to the permutation test, allowing significant reduction of computation time while preserving reported error rates used in multiple testing. The method is applied to gene-set analysis, using two sets of experimental data, and some advantage to a pathway modelling approach to this problem is reported.