From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases.

By A. G. de la Fuente. Trends in Genetics, 2010

441 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures (2017, methodology): “…inference for a given Gaussian graph, it is of significant interest in many applications to understand whether and how the network changes between disease states (de la Fuente 2010; Ideker and Krogan 2012; Hudson et al. 2009; Li et al. 2007; Danaher et al. 2014; Zhao et al. 2014; Xia et al. 2015).”
Inferring causal molecular networks: empirical assessment through a community-based effort (2016, background): “Further details of the simulation model can be found in Supplementary Note 8 .”
Next-Generation Machine Learning for Biological Networks. (2018, positive): “Similar approaches have been utilized in the context of network inference, where topological features are identified that can be attributable to differences in phenotypic observations at the expression level (de la Fuente, 2010; Mall et al., 2017).”