ArticlePloS one2014
Ensemble inference and inferability of gene regulatory networks.
Article in PloS one, 2014. 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.
- TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.BMC bioinformatics · 2024Article
- Improving dynamic predictions with ensembles of observable models.Bioinformatics (Oxford, England) · 2023Article
- Knowledge of the perturbation design is essential for accurate gene regulatory network inference.Scientific reports · 2022Article
- Bacteriophage self-counting in the presence of viral replication.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks.BMC bioinformatics · 2021Article
- Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants.Bioinformatics (Oxford, England) · 2019Article
- Improved pathway reconstruction from RNA interference screens by exploiting off-target effects.Bioinformatics (Oxford, England) · 2018Article
- Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection.Nucleic acids research · 2018Article
- SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles.Bioinformatics (Oxford, England) · 2018Article
- Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size.BMC systems biology · 2017Article
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.PLoS computational biology · 2017Article
- Inferring gene targets of drugs and chemical compounds from gene expression profiles.Bioinformatics (Oxford, England) · 2016Article
- TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments.BMC bioinformatics · 2016Article
- Optimal design of gene knockout experiments for gene regulatory network inference.Bioinformatics (Oxford, England) · 2016Article
- DTW-MIC Coexpression Networks from Time-Course Data.PloS one · 2016Article
- Identification of Metabolic Pathway Systems.Frontiers in genetics · 2016Article
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- A review on computational systems biology of pathogen-host interactions.Frontiers in microbiology · 2015Review
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2 authors.
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Abstract
The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge.
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