Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks

By Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young. Pacific Symposium on Biocomputing, 2000

448 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4073fbc7d8530d50f024f41a1bc95492fe490f9b

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Citations in the corpus

Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “Noteworthy, BANJO is a ready-to-use software application for BN and DBN inference (Hartemink et al., 2001).”
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana (2004, background): “Over the past few years, graphical models, in particular Bayesian networks, have become increasingly popular in reverse engineering of genetic regulatory networks [7-10].”
Co-clustering of biological networks and gene expression data (2002, unknown): “reasoning (Hartemink et al., 2001).”
Reverse engineering of regulatory networks in human B cells (2005, methodology): “Available methods fall into four broad categories: optimization method”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “…focus further on ﬁnding probabilistically supported gene interactions or on combining these into subnetworks 82,83 , on modeling ‘latent’ or hidden variables representing biological information unavailable to the model 84,85 , and on incorporating prior biological knowledge or annotation 82,86 .”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “…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 (SSMs; Hirose et al. 2008;…”
Genetic reconstruction of a functional transcriptional regulatory network (2007, background): “Several strategies of analyzing expression proﬁles by epistasis and network reﬁnement have been described 9–11 .”
A new approach to decoding life: systems biology. (2001, background): “cerevisiae Integrated physical-interaction (61, 68, 120) network, Bayesian networks”
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection (2012, methodology): “Moreover, running TIGRESS online is possible on GenePattern: http://www.broadinstitute.org/ cancer/software/genepattern/.”
Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks (2002, methodology): “…including linear models (van Someren et al., 2000; D'Haeseleer et al., 1999), Bayesian networks (Murphy and Mian, 1999; Friedman et al., 2000; Hartemink et al., 2001; Moler et al., 2000), neural networks (Weaver et al., 1999), differential equations (Mestl et al., 1995), and models including…”
Advances to Bayesian network inference for generating causal networks from observational biological data (2004, methodology): “1 INTRODUCTION A variety of network inference algorithms have recently been used to identify gene regulatory networks from observational gene expression data (Akutsu et al. 2000; Arkin et al. 1997; D'haeseleer et al. 1999; Friedman et al. 2000; Gardner et al. 2003; Hartemink et al. 2001; Liang et al”
Gene networks inference using dynamic Bayesian networks (2003, background): “Hartemink et al. (2001) focus on scoring the models they learn.”
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context (2004, methodology): “We implemented the BN algorithm in this work in accordance with [19,20].”
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.”
On Learning Gene Regulatory Networks Under the Boolean Network Model (2003, methodology): “, 1999), Bayesian networks (Murphy and Mian, 1999; Friedman et al., 2000; Hartemink et al., 2001), neural networks (Weaver et al.”
Fundamentals of cDNA microarray data analysis. (2003, background): “…of the state of a group of other genes [44–46] and a Bayesian network that models interactions among genes, evaluates different models and assigns them probability scores [47,48] (readers are referred to two excellent reviews on these and other methods for reverse engineering of networks [49,50]).”