ArticleBioengineering (Basel, Switzerland)2016
Stable Gene Regulatory Network Modeling From Steady-State Data.
Article in Bioengineering (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.PLoS computational biology · 2023Article
- An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information.Bioinformatics (Oxford, England) · 2023Article
- Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops.Frontiers in genetics · 2022Article
- Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers.Journal of translational medicine · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim to capture dependencies among molecular entities such as transcription factors, proteins and metabolites. In most applications, the regulatory network structure is unknown, and has to be reverse engineered from experimental data consisting of expression levels of the genes usually measured as messenger RNA concentrations in microarray experiments. Steady-state gene expression data are obtained from measurements of the variations in expression activity following the application of small perturbations to equilibrium states in genetic perturbation experiments. In this paper, the least absolute shrinkage and selection operator-vector autoregressive (LASSO-VAR) originally proposed for the analysis of economic time series data is adapted to include a stability constraint for the recovery of a sparse and stable regulatory network that describes data obtained from noisy perturbation experiments. The approach is applied to real experimental data obtained for the SOS pathway in
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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.