### Citations

4828 |
Pattern classification and scene analysis
- Duda, Hart
- 1972
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Citation Context ...2013; Jalali et al., 2011). 3.1.2 Exponential family The probability distributions that we study in this article are specific examples of a broad class of distributions called the exponential family (=-=Duda and Hart, 1973-=-). Members of the exponential family have many important properties in common. Given parameters h, the exponential family of distributions over X is defined to be the set of distributions of the form:... |

1046 | RNA-Seq: a revolutionary tool for transcriptomics - Wang - 2009 |

730 | High-dimensional graphs and variable selection with the lasso. The Annals of Statistics 34 - Meinshausen, Bühlmann - 2006 |

607 | Gene Ontology: tool for the unification of biology - Ashburner, Ball, et al. - 2000 |

591 | Sparse inverse covariance estimation with the graphical lasso - Friedman, Hastie, et al. - 2008 |

288 | A Markov random field model for term dependencies
- Metzler, Croft
- 2005
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Citation Context ...a, 1991). The problem of learning a network structure associated with an undirected graphical model has seen a wide range of applications ranging from social networks and image and speech processing (=-=Metzler and Croft, 2005-=-; Wang et al., 2013) to genomics. Applications in bioinformatics include estimation of molecular pathways from protein interaction and gene expression data (Segal et al., 2003; Stingo and Vannucci, 20... |

275 | The group Lasso for logistic regression - Meier, Geer, et al. |

260 |
Gaussian Markov random fields: theory and applications
- Rue, Held
- 2005
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Citation Context ...tics online. 1 Introduction Undirected graphical models or Markov networks are a popular class of statistical tools for probabilistic description of complex associations in high-dimensional data (cf. =-=Rue and Held, 2005-=-). Biological processes in a cell involve complex interactions between genes and it is important to understand, which genes conditionally depend on each other. These dependencies can be inferred from ... |

253 | A theory of inferred causation
- Pearl, Verma
- 1991
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Citation Context ...odels focuses on local dependencies between genes, where each gene is directly affected by a relatively small number of genes. Edges estimated by a graphical model can be related to causal inference (=-=Pearl and Verma, 1991-=-). The problem of learning a network structure associated with an undirected graphical model has seen a wide range of applications ranging from social networks and image and speech processing (Metzler... |

249 |
Machine Learning: A Probabilistic Perspective.
- Murphy
- 2012
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Citation Context ...tween genes. In particular, if there is no edge between genes s and t then this implies that the behavior of s is independent of t given the set of immediate neighbors of s. From this local property (=-=Murphy, 2012-=-), one can easily see that two genes (nodes) are conditionally independent given the rest of the genes iff there is no direct edge between them. The conditional independence (Markov) properties permit... |

237 | An Empirical Bayes Approach to Inferring Large-Scale - Sch€afer, Strimmer - 2005 |

175 | The genetic landscape of a cell - Costanzo, Baryshnikova, et al. |

151 | Discovering molecular pathways from protein interaction and gene expression data - Segal - 2003 |

111 | Defining and evaluating network communities based on ground-truth
- Yang, Leskovec
- 2012
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Citation Context ... members and few connections to the rest of the network. Four different structural notions of network communities exist in networks and we report the values of their representative scoring functions (=-=Yang and Leskovec, 2012-=-). We refer the reader to Supplementary Section 4 for mathematical details. 4.2 Considered gene network inference algorithms In the experiments, we consider the Poisson FUSENET (Section 3.3), the mult... |

92 | The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. - Liu, Lafferty, et al. - 2009 |

61 |
Network Inference using Informative Priors.
- Mukherjee, TP
- 2008
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Citation Context ...a (Segal et al., 2003; Stingo and Vannucci, 2011), reconstruction of gene regulatory networks from microarray data (Marbach et al., 2012), inference of a cancer signaling network from proteomic data (=-=Mukherjee and Speed, 2008-=-) and reconstruction of genetic interaction networks from integrated experimental data (Isci et al., 2014). Methods applied to these problems and many other recent gene network inference algorithms (A... |

58 | Highdimensional ising model selection using `1-regularized logistic regression. - Ravikumar, Wainwright, et al. - 2010 |

52 | International network of cancer genome projects. - Hudson - 2010 |

51 | Highdimensional semiparametric Gaussian copula graphical models. - Liu, Han, et al. - 2012 |

50 | Wisdom of crowds for robust gene network inference. - Marbach, Costello, et al. - 2012 |

22 | GATA3 acts upstream of FOXA1 in mediating ESR1 binding by shaping enhancer accessibility. Genome Res - Theodorou, Stark, et al. - 2013 |

21 | On learning discrete graphical models using group-sparse regularization. - Jalali, Ravikumar, et al. - 2010 |

19 | Sparse inverse covariance estimation with the lasso,” Biostatistics. - Friedman, Hastie, et al. |

19 | Graphical models via generalized linear models. - Yang, Allen, et al. - 2012 |

15 | Markov Random Field modeling, inference and learning in computer vision and image understanding: A survey. Computer Vision and Image Understanding - Wang, Komodakis, et al. - 2013 |

12 | Bayesian Gaussian copula factor models for mixed data - Murray, Dunson, et al. - 2013 |

10 |
Variable selection for discriminant analysis with markov random field priors for the analysis of microarray data
- Stingo, Vannucci
- 2011
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Citation Context ...Metzler and Croft, 2005; Wang et al., 2013) to genomics. Applications in bioinformatics include estimation of molecular pathways from protein interaction and gene expression data (Segal et al., 2003; =-=Stingo and Vannucci, 2011-=-), reconstruction of gene regulatory networks from microarray data (Marbach et al., 2012), inference of a cancer signaling network from proteomic data (Mukherjee and Speed, 2008) and reconstruction of... |

9 | A boosting approach to structure learning of graphs with and without prior knowledge - Anjum - 2009 |

9 | A proteome-scale map of the human interactome network. - Rolland - 2014 |

8 | Matrix factorization-based data fusion for gene function prediction in baker’s yeast and slime - Zitnik, Blaz - 2014 |

6 | On poisson graphical models. - Yang, Ravikumar, et al. - 2013 |

4 | Gene-gene interactions in breast cancer susceptibility.Hum.Mol.Genet.,21 - Turnbull, Seal, et al. - 2012 |

3 |
A local poisson graphical model for inferring networks from sequencing data
- Allen, Liu
- 2013
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Citation Context ...easurements about gene A and its immediate neighbors in a network. Hence, Markov networks can help us to find a rich set of direct dependencies between genes that are stronger than gene correlations (=-=Allen and Liu, 2013-=-). Markov networks have been well studied in bioinformatics and numerous applications are concerned with inferring the network structure primarily from microarray and next generation sequencing gene e... |

3 | Bayesian network prior: network analysis of biological data using external knowledge - Isci - 2014 |

3 | GENIES: gene network inference engine based on supervised analysis - Kotera - 2012 |

3 | Discovering disease-disease associations by fusing systems-level molecular data - Zitnik, Janjić, et al. - 2013 |

3 | B (2015) Data Fusion by Matrix Factorization - Zitnik, Zupan |

2 | Widespread genetic epistasis among cancer genes. - Wang - 2014 |

1 | Expression of FOXA1 and GATA3 in breast cancer: the prognostic significance in hormone receptor-negative tumours - Albergaria - 2009 |

1 | Utilizing RNA-seq data for cancer network inference - Cai - 2012 |

1 | SANTA: quantifying the functional content of molecular networks
- Cornish, Markowetz
- 2014
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Citation Context ...e of 0. 4.1.2 Quantifying the functional content of inferred networks We employ two approaches to evaluate the ‘functional correctness’ of the networks inferred from cancer data. First, we use SANTA (=-=Cornish and Markowetz, 2014-=-) to quantify the strength of association between sets of functionally related genes from the Gene Ontology (GO) (Ashburner et al., 2000) and the inferred network. Second, we overlay the inferred netw... |

1 | A hierarchical Poisson log-normal model for network inference from RNA sequencing data - Gallopin - 2013 |