Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control

Peña, José M.

doi:10.1007/978-3-540-78757-0_15

Cited by 25 publications

(28 citation statements)

References 24 publications

(40 reference statements)

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“…With this in mind, the ability to restrict the search locally around the target variable is a key advantage of CB methods over SS methods. They are able to construct a local graph around the target node without having to construct the whole BN first, hence their scalability (Peña et al, 2007;Rodrigues de Morais & Aussem, 2010b,a;Tsamardinos et al, 2006;Peña, 2008).…”

Section: Structure Learning Strategiesmentioning

confidence: 99%

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

Gasse

Aussem

Elghazel

2014

Expert Systems with Applications

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We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.

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Section: Structure Learning Strategiesmentioning

confidence: 99%

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

Gasse

Aussem

Elghazel

2014

Expert Systems with Applications

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“…More recent approaches are included in the works of Dobra, Hans, Jones, Nevins, Yao, and West (2004), (Castelo & Roverato, 2006), Peña (2008), and Schäfer and Strimmer (2005), that focus on applications of Gaussian graphical models in Bioinformatics. While we do not make the assumption of continuous Gaussian variables in this paper, all algorithms we present are applicable to such domains with the use of an appropriate conditional independence test (such as partial correlation).…”

Section: Related Workmentioning

confidence: 99%

Efficient Markov Network Structure Discovery using Independence Tests

Bromberg

Margaritis

Honavar

2006

Proceedings of the 2006 SIAM International Conference on Data Mining

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We present two algorithms for learning the structure of a Markov network from data: GSMN * and GSIMN. Both algorithms use statistical independence tests to infer the structure by successively constraining the set of structures consistent with the results of these tests. Until very recently, algorithms for structure learning were based on maximum likelihood estimation, which has been proved to be NP-hard for Markov networks due to the difficulty of estimating the parameters of the network, needed for the computation of the data likelihood. The independence-based approach does not require the computation of the likelihood, and thus both GSMN * and GSIMN can compute the structure efficiently (as shown in our experiments). GSMN * is an adaptation of the Grow-Shrink algorithm of Margaritis and Thrun for learning the structure of Bayesian networks. GSIMN extends GSMN * by additionally exploiting Pearl's well-known properties of the conditional independence relation to infer novel independences from known ones, thus avoiding the performance of statistical tests to estimate them. To accomplish this efficiently GSIMN uses the Triangle theorem, also introduced in this work, which is a simplified version of the set of Markov axioms. Experimental comparisons on artificial and real-world data sets show GSIMN can yield significant savings with respect to GSMN * , while generating a Markov network with comparable or in some cases improved quality. We also compare GSIMN to a forward-chaining implementation, called GSIMN-FCH, that produces all possible conditional independences resulting from repeatedly applying Pearl's theorems on the known conditional independence tests. The results of this comparison show that GSIMN, by the sole use of the Triangle theorem, is nearly optimal in terms of the set of independences tests that it infers.

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“…To illustrate this idea, consider modelling gene expression data, where entry X j is associated with the expression level of gene j . GGMs have been widely used in this context to identify pathways and define metagenes (e.g., see Dobra et al, 2004; Jones et al, 2005; Peña, 2008). However, recent studies suggest that in applications such as cancer genetics, the population can be more effectively described as a mixture of a small number of components, with each component presenting differentially expressed genes as well as different expression pathways (The Cancer Genome Atlas Network, 2012).…”

Section: Introductionmentioning

confidence: 99%

GPU-Powered Shotgun Stochastic Search for Dirichlet Process Mixtures of Gaussian Graphical Models

Mukherjee

Rodríguez

2016

Journal of Computational and Graphical Statistics

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Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful.

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Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control

Cited by 25 publications

References 24 publications

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

Efficient Markov Network Structure Discovery using Independence Tests

GPU-Powered Shotgun Stochastic Search for Dirichlet Process Mixtures of Gaussian Graphical Models

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