Inferring Cellular Networks Using Probabilistic Graphical Models

By Nir Friedman. 2004

1,236 citations recorded by the source corpus.

Subfields: Molecular Biology, Probabilistic & Bayesian Methods

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, background): “…representing uncertainty about the world in multiple scientiﬁc domains, including medical diagnosis (Lau-ritzen and Spiegelhalter, 1988; Heckerman and Nathwani, 1992), molecular biology (Friedman, 2004; Sebastiani et al., 2005), and ecological modeling (Varis and Kuikka, 1999; Marcot et al., 2006).”
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms (2006, neutral): “rithms that infer the structure of transcriptional regulatory networks (TRNs) from this data [2-6].”
Inverse statistical problems: from the inverse Ising problem to data science (2017, neutral): “For reviews see [74, 107].”
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks (2008, background): “To this end, an extensive literature on the problem of gene regulatory network “reverse engineering” has developed in the past decade (Friedman, 2004).”
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].”
Statistical inference of the time-varying structure of gene-regulation networks (2010, background): “From available data, in-silico methods can generate hypotheses about biochemical and molecular mechanisms [6,7] and guide further experimental and theoretical investigations into regulatory interactions underlying biological systems.”
Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network (2009, background): “Networks or graphs have been extensively studied as a representation of the correlation structure of phenotypes such as gene expression or clinical traits because they provide a flexible and explicit form of representation for capturing dependencies [39–41].”
Regularized rank-based estimation of high-dimensional nonparanormal graphical models (2012, background): “…noncausal structured approach to explore the complex systems consisting of Gaussian random variables, and it ﬁnds many interesting applications in areas such as gene expression genomics and macroeconomics determinants study [Friedman (2004), Wille et al. (2004), Dobra, Eicher and Lenkoski (2010)].”
Learning Causal Bayesian Network Structures From Experimental Data (2008, background): “The Bayesian Network (BN) is a class of multivariate statistical models applicable to many areas in science and technology (Beinlich, Suermondt, Chavez and Cooper 1989; Pearl 1988; Pe´er 2005; Friedman 2004).”
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context (2004, background): “For example, graphical modeling [4] defines edges as parent-child relationships between mRNA abundance levels that are most likely to explain the data, integrative methods [5] use independent experimental clues to define edges as those showing evidence of physical interactions, and other statistical”
Causality : Objectives and Assessment (2010, background): “For a review of Friedman’s work see (Friedman, 2004).”
Machine learning in genetics and genomics (2015, methodology): “More sophisticated methods attempt to jointly model the expression of all of the genes in a cell by training a network model 13 .”
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/.”
Pathway level analysis of gene expression using singular value decomposition (2005, background): “We suggest that pathway activity levels may also be useful in other areas, for example, as variables in models for gene regulation (e.g., Bayesian networks [25]), for clustering and classification [15,26], and in computational efforts to discover novel gene-path-way associations.”