Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks

By Dirk Husmeier. 2003

549 citations recorded by the source corpus.

Subfields: Molecular Biology, Probabilistic & Bayesian Methods, Computational Biology & Bioinformatics

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Citations in the corpus

Advances to Bayesian network inference for generating causal networks from observational biological data (2004, methodology): “Bayesian network (BN) inference algorithms have shown particular promise (Harteminket al., 2002; Husmeier, 2003; Smithet al., 2002, 2003), because unlike most other modeling frameworks, they can capture many types of relationships between variables.”
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana (2004, methodology): “The performance of the graphical modeling approaches was monitored using the rate of true and false positives in receiver operator characteristics (ROC) curves (see [11] for a short introduction).”
Advances in Exact Bayesian Structure Discovery in Bayesian Networks (2006, methodology): “…(number of attributes, number of observations, maximum indegree, number of states per attribute).1 Our study is complementary to a previous work by Husmeier (2003) that uses a Markov chain Monte Carlo method and focuses on very small data sets and large, yet very sparse, dynamic Bayesian networks.”
Partition MCMC for Inference on Acyclic Digraphs (2015, background): “Think for instance of gene regulatory networks (Friedman et al. 2000; Husmeier 2003; Friedman 2004; Rau et al. 2012) and cellular signaling pathways (Sachs et al. 2005; Mukherjee and Speed 2008; Hill et al. 2012), including their development over time (Husmeier 2003; Hill et al. 2012; Rau et al.…”
Stochastic modelling for quantitative description of heterogeneous biological systems (2009, methodology): “Techniques used so far include methods for learning of dynamic Bayesian networks applied to discretized dat”
An empirical Bayes approach to inferring large-scale gene association networks (2005, background): “In practice however, owing to the paucity of the data at hand, it is not generally possible to infer these non-linearities nor the global network structure (Husmeier, 2003; Friedman and Koller, 2003).”
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms (2006, unknown): “Several models have been proposed for this purpose, including Boolean [14,15], continuous [7,9,11] and probabilistic [11] approaches.”
Inferring cellular networks – a review (2007, methodology): “Husmeier [85] uses the same simulation network [87] to specify sensitivity and specificity of dynamic Bayesian networks.”
Comparing Statistical Methods for Constructing Large Scale Gene Networks (2012, background): “the intuition is that, as the number of samples increases, the algorithms would be able to perform better [41].”
Learning Bayesian networks: approaches and issues (2011, background): “These ideas have been built on by Husmeier (2003) and other researchers (Aitken et al., 2005) who look at the problem of small sample sizes prevalent with biological data and examine techniques to characterize the sensitivity and specificity of results.”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “, those based on synchronization [253], Bayesian estimation [254], and correlation between noisy signals [27].”