Modelling Gene Expression Data using Dynamic Bayesian Networks

By Kevin P. Murphy. 2006

439 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/811d45b093a63a3d19890b0bed9fdd327c6fce87

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Citations in the corpus

• Learning Bayesian networks: approaches and issues (2011, background): “Murphy and Mian (1999) show how DBNs subsume many other dynamic models into a general framework and look at the various tasks that need to be done to learn a DBN.”
• Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, methodology): “…Fuhrman, and Somogyi 1998; Akutsu, Miyano, and Kuhara 2000; Shmulevich et al. 2002; Martin et al. 2007), dynamic Bayesian networks (Murphy and Mian 1999; Friedman et al. 2000; Hartemink et al. 2001; Zou and Conzen 2005; Song, Kolar, and Xing 2009), vector autoregressive and state space models…”
• A Primer on Learning in Bayesian Networks for Computational Biology (2007, methodology): “When BNs are used to model time series and feedback loops, the variables are indexed by time and replicated in the BN—such networks are known as dynamic Bayesian networks (DBNs) [12] and include as special cases hidden Markov models (HMMs) and linear dynamical systems.”
• Inferring cellular networks – a review (2007, background): “If we assume that Xv at time t + 1 can only have parents at time t, then cycles "unroll" and the resulting model is again acyclic and tractable: it is called a Dynamic Bayesian network (DBN) [52,53].”
• On Learning Gene Regulatory Networks Under the Boolean Network Model (2003, methodology): “…approaches to gene regulatory network modeling have been introduced, including linear models (D'Haeseleer et al., 1999), Bayesian networks (Murphy and Mian, 1999; Friedman et al., 2000; Hartemink et al., 2001), neural networks (Weaver et al., 1999; Vohradsky, 2001), differential equations…”