The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

By Richard Bonneau, David J. Reiss, Paul T. Shannon, Marc T. Facciotti, Leroy Hood, Nitin S. Baliga, Vesteinn Thorsson. Genome Biology, 2006

572 citations recorded by the source corpus.

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b78993c6475d6a4da8ba8bf0a1ad0bbb1a5ba688

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Citations in the corpus

How to infer gene networks from expression profiles (2007, methodology): “Other algorithms based on ODEs have been proposed in the literature (D’haeseleer et al , 1999; Tegner et al , 2003; Bonneau et al , 2006; van Someren et al , 2006).”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, methodology): “…used machine-learning algorithms to identify cis -regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genome-wide location analysis chromatin immunoprecipitation (ChIP-Chip) [19,20], or a combination of these and other high-throughput methods…”
Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota (2013, methodology): “A related approach based on linear ordinary differential equations has already been applied to gene-interaction networks [41–44].”
Advantages and limitations of current network inference methods (2010, background): “This information can be helpful for designing the appropriate conditions under which experimental validation of the predicted interactions should be performe”
Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, methodology): “Second, SPIEC-EASI estimates the interaction graph from the transformed data using one of two methods: (i) neighborhood selection [20, 21] and (ii) sparse inverse covariance selection [22, 23].”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “The linear regression models for learning regulatory networks assume that expression level of a gene can be approximated by a linear combination of the expressions of other genes [128, 129, 133, 149], and such models form a middle ground between the models based on differential equations and Boolean”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, background): “Nevertheless, hybrid methods involving ordinary differential equations have shown good performances on real-life genome-wide GRN inference [8].”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “…and information theoretical 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,…”
Quantitative and logic modelling of gene and molecular networks (2015, methodology): “Finally, methods based on ordinary differential equations (ODEs) (76) (for example, Inferelator (77) and NIR (78)) are particularly suited to time-course data and aim at inferring quantitative and dynamic interactions between genes and molecules.”