Using Bayesian Networks to Analyze Expression Data

By Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er. Annual International Conference on Research in Computational Molecular Biology, 2000

3,690 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c84539a89bc0a2a2425ca1b0046833f57d86e7b0

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Citations in the corpus

Advances to Bayesian network inference for generating causal networks from observational biological data (2004, background): “They can effectively handle hundreds of variables (Friedman et al., 2000; Smithet al., 2002).”
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, background): “…into relational patterns among observed variables in a large variety of disciplines such as statistical mechanics (Albert and Barabasi 2002), biology (Friedman, Linial, Nachman, and Pe’er 2000), genetics (Ghazalpour, Doss, Zhang, Wang, Plaisier, Castellanos, Brozell, Schadt, a r X i v : 1510 .”
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana (2004, background): “Even a search for local dependence structures and subnetworks with high statistical support [7] provides no guarantee against the detection of numerous spurious features.”
Current approaches to gene regulatory network modelling (2007, background): “Bayesian networks provide a probabilistic framework for modelling gene regulatory networks [48-50].”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, neutral): “This research design follows Friedman et al. (2000).”
A computational algebra approach to the reverse engineering of gene regulatory networks. (2003, methodology): “Bayesian network methods, first proposed in (Friedman et al., 2000), and further developed in (Hartemink et al., 2001), can be applied to single time points.”
Discovering causal interactions using Bayesian network scoring and information gain (2016, background): “They have been applied to many domains including biomedical informatics [41–46].”
Structure learning in causal cyclic networks (2008, background): “As with other efforts to learn static representations of underlying dynamic systems (Friedman et al., 2000; Sachs et al., 2005), what we propose here can be interpreted as learning a DBN or CTBN in the absence of time-course data, or from single time-point data (constituting a snapshot of a dynamic…”
Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering (2002, background): “A number of methods have been developed to deal with complex relationships between objects [9-11].”
Harnessing naturally randomized transcription to infer regulatory relationships among genes (2007, background): “Model selection approaches The concepts of 'causality' and 'regulation' have been utilized in different ways in previous reports concerning the construction of biologic networks [29,30,32,56-60].”
Gene networks inference using dynamic Bayesian networks (2003, methodology): “Static Bayesian networks have been used by Friedman et al. (2000) for analyzing gene networks. A linear gene interaction model is considered. Hartemink et al. (2001) focus on scoring the models they learn. Static Bayesian networks have also been considered by Pe’er et al. (2001), and are learned fro”
Inferring gene regulatory networks from time series data using the minimum description length principle (2006, neutral): “…(also referred to as PBNs), the use of Bayesian networks (Pearl, 1988) to model non-temporal probabilistic dependency relations among genes (Friedman et al ., 2000) and the use of dynamic Bayesian networks (DBNs) (Dean and Kanazawa, 1989) to model temporal stochastic relations among genes…”