Gene networks inference using dynamic Bayesian networks

Perrin, Bruno-Edouard; Ralaivola, Liva; Mazurie, Aurélien; Bottani, Samuele; Mallet, Jacques; d’Alché‐Buc, Florence

doi:10.1093/bioinformatics/btg1071

Cited by 369 publications

(219 citation statements)

References 17 publications

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“…DBN learning approaches have been applied to biological data of different kinds, e.g. gene expression profiles (Murphy and Mian 1999;Friedman et al 2000;Perrin et al 2003;Kim et al 2004;Zou and Conzen 2005), fMR images (Junning Li and McKeown 2006;Li et al 2007;Rajapakse and Zhou 2007;Rajapakse et al 2008;Burge et al 2009), and also neural electrophysiological multi-unit data (Smith et al 2006). So far the only publication of which we are aware applying DBNs to (simulated) spike train data is Eldawlatly et al (2008), who used a coarse representation of 3 ms time-bins for the data.…”

Section: Discussionmentioning

confidence: 99%

Causal pattern recovery from neural spike train data using the Snap Shot Score

2009

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We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

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Section: Discussionmentioning

confidence: 99%

Causal pattern recovery from neural spike train data using the Snap Shot Score

2009

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“…[12][13][14][15] most of which use the so-called coefficient of determination (CoD) principle [47]. Learning methods for DBNs, in the context of genetic networks, have been studied in [17,21,22,24,25,27,29]. Note, however, that some of the DBN studies have concentrated on non-temporal BNs.…”

Section: Discussionmentioning

confidence: 99%

“…DBNs and their non-temporal versions, BNs, have successfully been used in different engineering problems, such as in speech recognition [19] and target tracking and identification [20]. Recently, BNs have also been used in modeling genetic regulation [17,[21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

et al. 2006

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A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes.

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“…For the discrete gene expression data (expressed: 1, unexpressed: 0), models like Boolean network (BN), probabilistic Boolean network (PBN), etc. have been proposed [7][8][9] , while for the continuous expression data, Pearson's correlation based algorithm 10,11 , Bayesian network modelling 12 , dynamic Bayesian network modelling approach 13 , etc. have been developed for network inference.…”

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confidence: 99%

Inferring Gene Regulatory Networks Using Kendall's Tau Correlation Coefficient and Identification of Salinity Stress Responsive Genes in Rice

S¹,

Meher²,

Pradhan³

et al. 2017

Current Science

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Salinity is one of the most common abiotic stresses that limit the production of rice. Since salinity stress tolerance is controlled by many genes, identification of these stress responsive genes as well as to understand the underlying mechanisms is of importance from breeding point of view. In this direction, the reverse engineering of gene regulatory networks has proven to be successful. In this study, we construct the gene regulatory network using Kendall's tau correlation coefficient, in order to identify the stress responsive genes. The proposed approach was tested on a rice microarray dataset and 18 key genes were identified. Most of these key genes were found to be involved directly or indirectly in salinity stress, as evidenced from the published literature. Gene ontology analysis further confirmed the involvement of the selected genes in ion binding, oxidation-reduction and phosphorylation activities. These identified genes can be targeted for breeding salt-tolerant varieties of rice.

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Gene networks inference using dynamic Bayesian networks

Cited by 369 publications

References 17 publications

Causal pattern recovery from neural spike train data using the Snap Shot Score

Causal pattern recovery from neural spike train data using the Snap Shot Score

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

Inferring Gene Regulatory Networks Using Kendall's Tau Correlation Coefficient and Identification of Salinity Stress Responsive Genes in Rice

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