Reverse engineering cellular networks

By Adam A. Margolin, Kai Wang, W. Lim, Manjunath Kustagi, Ilya Nemenman, Andrea Califano. Nature Protocols, 2006

388 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/11f5d811258625b3a46451655d81f5b26f1c38ca

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

Citations in the corpus

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “…approaches have been used for network inference from population level data (Penfold et al. 2011, Penfold et al. 2015, Bonneau et al. 2006, Margolin et al. 2006a, Villaverde et al. 2013, Villaverde et al. 2013, Liang et al. 2008, Madar et al. 2010, Hill et al. 2012, Lèbre et al. 2010,…”
Integration of biological networks and gene expression data using Cytoscape (2007, methodology): “Cytoscape can be used in many additional visualization and analysis workflows, including published protocols on the reverse engineering of regulator”
The genetic basis of early T-cell precursor acute lymphoblastic leukaemia (2012, methodology): “Furthermore, reconstruction of the transcriptional network of ETP ALL using ARACN”
Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation (2014, methodology): “To define these common macrophage activators, we used reverse network engineering (RNE) utilizing ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks, Figure 6A) (Margolin et al., 2006).”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, methodology): “For ARACNE, the tolerance parameter was optimized between 0 and 15%, as advised in [12].”
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. (2017, positive): “RTN regulons are additionally evaluated by the Data Processing Inequality (DPI) algorithm with tolerance=0.01 (Margolin et al., 2006).”
Benchmark and integration of resources for the estimation of human transcription factor activities (2018, methodology): “in silico from large-scale gene expression profiles derived from a condition of interest (Rice et al. 2005;Margolin et al. 2006).”