Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

By Ilya Shmulevich, Edward R. Dougherty, Seungchan Kim, Wei Zhang. Bioinform., 2002

1,680 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a5d3fa5b1c482e5a1651174d047ce2e66989a686

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In Molecular Biology

Citations in the corpus

Reconstruction of Biochemical Networks in Microbial Organisms (2008, background): “Most large-scale regulatory network models built so far have used Boolean network approaches and a range of probabilistic modelling frameworks, including simplified additive kinetic modelling approaches using, for example, log-linear kinetic”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “…among others (Liang, Fuhrman, and Somogyi 1998; Murphy and Mian 1999; Akutsu, Miyano, and Kuhara 2000; Friedman et al. 2000; Hartemink et al. 2001; Shmulevich et al. 2002; Zou and Conzen 2005; Gupta, Qu, and Ibrahim 2007; Martin et al. 2007; Hirose et al. 2008; Kojima et al. 2009; Shimamura et…”
On Learning Gene Regulatory Networks Under the Boolean Network Model (2003, methodology): “In order to capture the dynamic nature of the network, it may be useful to consider the wiring diagram (see e.g. (Akutsu et al., 1999; Shmulevich et al., 2002a; Shmulevich et al., 2002b)) which gives an explicit way of implementing the updating procedure.”
Controllability of Boolean control networks with impulsive effects and forbidden states (2014, methodology): “It was developed by [2], Farrow et al.”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “A (probabilistic) Boolean network [52] is a special case of a discrete”
The value of prior knowledge in machine learning of complex network systems (2016, positive): “We use randomly generated Boolean networks [8, 9] as means of producing large datasets to apply machine learning algorithms to.”
Current approaches to gene regulatory network modelling (2007, neutral): “[73].”
Analysis and control of Boolean networks: A semi-tensor product approach (2010, methodology): “The Boolean network, introduced firstly by Kauffman [14], and then developed by [2, 3, 10, 11, 18, 19] and many others, becomes a powerful tool in describing, analyzing, and simulating the cell networks.”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “These methods can be categorized into machine learning-based 73 – 75 , co-expression-based 76 , model-based 77,78 , and information theory-based approaches.”
From Boolean to probabilistic Boolean networks as models of genetic regulatory networks (2002, background): “In [34], a relationship between PBNs and Bayesian networks”
Controllability of Boolean control networks via the Perron-Frobenius theory (2012, background): “Key words: Logical systems, reachability, controllability, gene regulating networks, systems biology, nonnegative matrices.”
Gene perturbation and intervention in probabilistic Boolean networks (2002, methodology): “Boolean networks and PBNs also exhibit this behavior, the former with fixed point and limit cycle attractors (Kauffman, 1993), the latter with absorbing states and irreducible sets of states (Shmulevich et al., 2002).”