Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

By Jun Zhu, Bin Zhang, Erin N. Smith, B. Drees, Rachel B. Brem, Leonid Kruglyak, Roger E Bumgarner, Eric E. Schadt. Nature Genetics, 2008

590 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8d1c7cbc051dc5d27fdddccabfc702ddb8fe4847

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Citations in the corpus

Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, methodology): “The analysis of mediating causal genes is of considerable interest in itself and methods to identify mediating genes, such as the Trigger algorithm [20] and related methods [54, 55], have been described elsewhere.”
Methods of integrating data to uncover genotype–phenotype interactions (2015, methodology): “Finally, network-based approaches have been developed in which a Bayesian network is constructed using gene expression data, metabolomic data and SNP genotype data, followed by integration to construct probabilistic causal networks 66–68 .”
ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization (2015, background): “There are increasing evidences shown that combinations of TFs are important for regulating gene expression (Perez-Pinera et al., 2013; Zhu et al., 2008).”
Time-Varying Dynamic Bayesian Networks (2009, methodology): “More specifically, we use three types of information to define the gene sets: transcription factor binding targets (TF), gene knockout signatures (Knockout), and gene ontology (Ontology) groups [24].”
Molecular networks as sensors and drivers of common human diseases (2009, background): “but also more generally allow the construction of predictive gene network”
Statistical inference of the time-varying structure of gene-regulation networks (2010, background): “Molecular interactions and regulatory networks underlie the development and functioning of biological systems [1-3].”
Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network (2009, methodology): “In several previous studies that incorporated a gene co-regulation network in a genome-wide scan for associations [2,10,11], a heuristic procedure was employed that combines results from two separate analyses, one being traditional single-SNP/single-trait association tests and the other being an ad…”
Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso (2010, background): “For example, in genetic association a nalysis, where the goal is to discover few genetic variants or single neucleotide polymorphisms (SNP s) out of millions of SNPs (inputs) that influence phenotypes (outputs) such as gene expression meas urements [23], groups of genes in the same pathways are more”
Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems (2008, methodology): “The obvious next step for these analyses is to link the network structure revealed by epistasis analysis to information obtained from other methods, such as yeast two-hybrid, chromatin immunoprecipitation and gene expression assays, to build a comprehensive map of the full 'interactome”
Heterogeneous multitask learning with joint sparsity constraints (2009, unknown): “Over the recent years, researchers started recognizing the importance of the joint analysis of multiple correlated phenotypes [5] [18], but there has been a lack of statistical tools to systematically perform such analysis.”
Inverse statistical problems: from the inverse Ising problem to data science (2017, methodology): “Such an approach has already been used in gene regulatory networks [187, 254], and with the expansion of tools to analyze large numbers of single cells [43, 249], it may soon spread to other types of networks.”
Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses (2012, methodology): “For example, other bilinear models have previously been used by Biswas et al. 33 , and methods to combine eQTL mapping with integrated network models have been considered by Zhu et al. 34 and Aten et al. 35 .”