Advantages and limitations of current network inference methods

By Riet De Smet, Kathleen Marchal. Nature Reviews Microbiology, 2010

528 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8d1f074027a821552fb12cc892ce129223fce0ca

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In Molecular Biology

Citations in the corpus

ScholarWorks @ UTRGV ScholarWorks @ UTRGV (methodology): “As each method has its own advantages and limitations under different assumptions and network models such as acyclic or cyclic network and directed or undirected network, there should be trade-offs in inferences given different target network structure and applications [10].”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “A thorough review of network methods is beyond our scope and we point the reader to the many many review articles [37-40].”
A scalable SCENIC workflow for single-cell gene regulatory network analysis (2020, background): “This additional computational step reduces the false-positive rate of predicted targets and makes SCENIC an integrative method 25 .”
Quantitative and logic modelling of gene and molecular networks (2015, unknown): “Comprehensive surveys of existing network inference methods and tools have been undertaken elsewhere (65,66).”
Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures (2015, background): “This fact has, in part, motivated computational biologists to develop a large collection of algorithms to infer aspects of this structure [1,42] and experimental biologists to compare networks on the basis of their hierarchical and modular architecture [43].”
Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information (2012, background): “Although many popular network inference algorithms have been investigated (Altay and Emmert-Streib, 2010; Bansal et al. 2007), there are still a large space for current models to be improved (Marbach et al., 2010; Smet and Marchal, 2010).”
Inferring causal molecular networks: empirical assessment through a community-based effort (2016, methodology): “Several submissions used novel methods or incorporated novel adaptations of existing methods ( Supplementary Tables 2 and 4 ).”