ArticleIET systems biology2013
Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data.
Article in IET systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- In silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimisation approach.IET systems biology · 2015Trial
- ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.Bioinformatics (Oxford, England) · 2023Article
- LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.BMC bioinformatics · 2020Article
- Quantifying Gene Regulatory Relationships with Association Measures: A Comparative Study.Frontiers in genetics · 2017Article
- Crosstalk between pathways enhances the controllability of signalling networks.IET systems biology · 2016Article
- Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.IET systems biology · 2016Article
- Identification of cancer-related genes and motifs in the human gene regulatory network.IET systems biology · 2015Article
- Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.Current genomics · 2015Article
- A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways.Journal of the Royal Society, Interface · 2015Article
- RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse.Database : the journal of biological databases and curation · 2015Article
- Using network clustering to predict copy number variations associated with health disparities.PeerJ · 2015Article
- PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data.Nucleic acids research · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge-based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.