Inferring gene networks from time series microarray data using dynamic Bayesian networks

By SunYong Kim, Seiya Imoto, Satoru Miyano. Briefings Bioinform., 2003

346 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fa8cdc1ca4d316f6123c8bffad98a0bfc9c34aef

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Citations in the corpus

Network inference using steady-state data and Goldbeter-Koshland kinetics (2012, methodology): “To date, most methods for network inference have been rooted in discrete or linear formulations (Bender et al., 2010; Sachs et al., 2005; Morrissey et al., 2010; Opgen-Rhein and Strimmer, 2007; Hill, 2012; Kim et al., 2003).”
Advances in Bayesian network modelling: Integration of modelling technologies (2019, background): “A variant of DBNs are those operating in real time in response to discrete or continuous inputs, such as for predicting highway crashes (Hossain and Muromachi, 2012) and in analyzing gene networks (Kim et al., 2003).”
Modelling and analysis of gene regulatory networks (2008, methodology): “The time-dependent transcription factor activities are inferred from microarray time-series data using dynamic Bayesian network”
Network Inference and Biological Dynamics (2011, background): “…Van Veen and Nowak (2011); Morrissey et al. (2010); Opgen-Rhein and Strimmer (2007); Zou and Feng (2009)], DBNs [Hill, Lu and Molina (2011); Kim, Imoto and Miyano (2003)] or ODE-based approaches [Bansal and di Bernardo (2007); Li and Chen (2010); Nam, Yoon and Kim (2007)], although as we…”
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data (2005, background): “Dynamic Bayesian network (DBN) analysis (Murphy and Mian, 1999; Imoto et al., 2002; Kim et al., 2003; Perrin et al., 2003) is well-suited for handling time-series gene expression data.”