ArticleNucleic acids research2013
Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A gene regulatory network inference model based on pseudo-siamese network.BMC bioinformatics · 2023Article
- Degree of Freedom of Gene Expression in Saccharomyces cerevisiae.Microbiology spectrum · 2022Article
- RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.Communications biology · 2022Article
- An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests.Scientific reports · 2021Article
- NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles.G3 (Bethesda, Md.) · 2020Article
- Genome-wide dynamic network analysis reveals a critical transition state of flower development in Arabidopsis.BMC plant biology · 2019Article
- An omnidirectional visualization model of personalized gene regulatory networks.NPJ systems biology and applications · 2019Article
- MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data.BMC systems biology · 2018Article
- Improving GRN re-construction by mining hidden regulatory signals.IET systems biology · 2017Article
- An integrative method to decode regulatory logics in gene transcription.Nature communications · 2017Article
- Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data.PloS one · 2017Article
- Inference of Gene Regulatory Network Based on Local Bayesian Networks.PLoS computational biology · 2016Article
- Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL.PloS one · 2016Article
- Review
- Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.Nucleic acids research · 2015Article
- MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation.Journal of immunology research · 2015Article
- Getting to the edge: protein dynamical networks as a new frontier in plant-microbe interactions.Frontiers in plant science · 2014Review
- Mining seasonal marine microbial pattern with greedy heuristic clustering and symmetrical nonnegative matrix factorization.BioMed research international · 2014Article
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Authors and funding
8 authors.
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Abstract
The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon's mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments.
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