Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling

By T. Gardner, D. di Bernardo, David Lorenz, James J. Collins. Science, 2003

1,207 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2d3c16cd34615f8ad7b7b6b8060113361bc810fe

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

The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “Indeed, such processes have been validated in small sets of discrete gene networks active in organisms, such as bacteria, 58 yeast, 59 and Drosophila , 60 as well as for mapping networks of small noncoding RNA in bacteria.”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, positive): “) in M experiments and then solve the system of equations, as done in the Network Inference by Regression (NIR) algorithm [Gardner et al., 2003].”
Revealing strengths and weaknesses of methods for gene network inference (2010, neutral): “According to a survey that we conducted among the participants, the applied inference methods span a wide range of approaches commonly used to reverse engineer gene networks, including correlation-based methods (6), information-theoretic methods (9, 27), Bayesian network predictions (4), and methods”
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”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, background): “…interaction networks and their potential roles in disease mechanism and drug development [1, 4–7], including transcriptional and post-transcriptional regulatory networks [8–10], functional RNA networks [11–13], protein–protein interaction networks [14, 15], and metabolic networks [16, 17].”
Causal network inference using biochemical kinetics (2014, neutral): “Figure 1 provides an outline of the workflow below.”
Molecular ecological network analyses (2012, methodology): “and proteins, differential equation-based network methods [9–12], Bayesian network methods [13,14], and rele-”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, background): “These models, typically based on systems of ordinary or stochastic differential equations, can generate realistic behavior [7].”
Constructing, conducting and interpreting animal social network analysis (2015, background): “2013), genetic networks (Gardner et al. 2003) and community-level linkages (Montoya, Pimm & Sole”
Discovery of meaningful associations in genomic data using partial correlation coefficients (2004, methodology): “However, these methods require specific and complicated experiments (Gardner et al., 2003), while the present method can be applied to a wider variety of experimental data.”