ArticleBMC systems biology2010
Statistical inference of the time-varying structure of gene-regulation networks.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.PloS one · 2011Pooled it
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- Uncovering Dynamic Neural Information Flow with Continuous-Time Weighted Dynamic Bayesian Networks.bioRxiv : the preprint server for biology · 2026Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
- Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework.Briefings in bioinformatics · 2024Article
- Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence.Stats · 2024Article
- Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context.Scientific reports · 2024Article
- MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images.PLoS computational biology · 2023Article
- Gene regulatory network inference based on a nonhomogeneous dynamic Bayesian network model with an improved Markov Monte Carlo sampling.BMC bioinformatics · 2023Article
- Temporal networks in biology and medicine: a survey on models, algorithms, and tools.Network modeling and analysis in health informatics and bioinformatics · 2023Review
- Experimental guidance for discovering genetic networks through hypothesis reduction on time series.PLoS computational biology · 2022Article
- A new Bayesian piecewise linear regression model for dynamic network reconstruction.BMC bioinformatics · 2021Article
- Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters.Bioinformatics (Oxford, England) · 2020Article
- TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.Journal of applied statistics · 2020Article
- Estimation of Dynamic Networks for High-Dimensional Nonstationary Time Series.Entropy (Basel, Switzerland) · 2019Article
- Predicting gene regulatory interactions based on spatial gene expression data and deep learning.PLoS computational biology · 2019Article
- Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices.Bioinformatics (Oxford, England) · 2019Article
- Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control.Journal of the American Statistical Association · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundBiological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.
methodsTo overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).
resultsWe demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning.
conclusionsARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.
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.