Inferring subnetworks from perturbed expression profiles
By Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman. Intelligent Systems in Molecular Biology, 2001
622 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/82a689c9f04218191b7a9b7c4936d8af48371f5d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Florian Markowetz
- Rainer Spang
- Aviv Regev
- Oliver Stegle
- John C. Marioni
- Alvis Brazma
- Sarah A. Teichmann
- Thomas Schlitt
Citations in the corpus
- Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data (2003, methodology): “Similar to previous methods for inferring regulatory networks from gene expression dat”
- BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA (2011, methodology): “Several methods have been applied for network inference based on genomics data, including Bayesian Networks (BNs) [Pe’er et al. (2001); Pe’er (2005)], Markov Networks (MNs, also called Markov random fields) [Wei and Li (2007, 2008)], and Dependency Networks (DNs) [Heckerman et al. (2000)].”
- Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks (2008, background): “Discussion Previous yeast network reconstructions have focused on a more limited number of genes in order to make the reconstruction tractabl”
- From patterns to pathways: gene expression data analysis comes of age (2002, methodology): “More recent modifications of Bayesian network methods focus further on finding probabilistically supported gene interactions or on combining these into subnetwork”
- Modeling and Simulation of Genetic Regulatory Systems: A Literature Review (2002, positive): “A recent extension of the method (Pe’er et al., 2001) is able to deal with genetic mutations and considers additional features, like activation, inhibition, and mediation relations between variables.”
- Inferring cellular networks – a review (2007, methodology): “Ideal interventions were applied in Bayesian networks [3,68,94], factor graphs [51] and dependency networks [28].”