Reverse engineering of regulatory networks in human B cells

By Katia Basso, Adam A. Margolin, Gustavo A. Stolovitzky, U. Klein, R. Dalla-Favera, Andrea Califano. Nature Genetics, 2005

1,474 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0dde092a85ce6451fc28685e7816a0df9dbaa21b

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Citations in the corpus

Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets (2008, neutral): “(2005) [88] B cells 15778709 10 66 HGU95Av2 12,625 12,104 13,134 9,364”
Inferring cellular networks – a review (2007, methodology): “[34] show that their own method, ARACNe, compares favorably against static Bayesian networks on a simulated network with 19 nodes [54] – but only if the dataset includes several hundreds of observations.”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “Previously, microarray-based bulk RNA-seq was utilized to uncover these networks 69,70 , although scRNA-seq has been more recently applied for this purpose 71 .”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, neutral): “ARACNE was designed for inference of large networks (of the order of thousands of genes), and it is not directly comparable to the other two approaches (Basso et al., 2005).”
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008, methodology): “In particular, mutual information networks have been succesfully applied to transcriptional network inference [4-6].”
A brassinosteroid transcriptional network revealed by genome-wide identification of BESI target genes in Arabidopsis thaliana. (2011, methodology): “For this purpose, we employed ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks), which was developed and successfully used to infer a GRN in human B cells (Basso et al., 2005).”
Mutational loss of PTEN induces resistance to NOTCH1 inhibition in T-cell leukemia (2007, methodology): “0 arrays according to the manufacturer's instructions as previously describe”
Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network (2009, background): “Differences between these syndromes involve a complex interplay of a large number of genomic variations that perturb the function of disease-related genes in the context of a regulatory network, rather than each gene individually [1,2].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Gene expression profiles may be used for the reverse engineering of cancer specific regulatory networks (Basso et al., 2005; Ergün et al., 2007).”
Comparing Statistical Methods for Constructing Large Scale Gene Networks (2012, methodology): “Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structur”