ArticleInterface focus2011
How to infer gene networks from expression profiles, revisited.
Article in Interface focus, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 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
65 citing papers in PubMed.
- Improved gene regulatory network inference from single cell data with dropout augmentation.PLoS computational biology · 2025Article
- From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
- Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data.NPJ systems biology and applications · 2024Article
- Transcriptional signatures of wheat inflorescence development.Scientific reports · 2022Article
- Gaining confidence in inferred networks.Scientific reports · 2022Article
- Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models.Frontiers in bioinformatics · 2022Article
- Causal network inference from gene transcriptional time-series response to glucocorticoids.PLoS computational biology · 2021Article
- Comparative transcriptomics reveals desynchronisation of gene expression during the floral transition between Arabidopsis andQuantitative plant biology · 2021Article
- Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks.Scientific reports · 2020Article
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- Gene regulatory network inference from sparsely sampled noisy data.Nature communications · 2020Article
- A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.BMC bioinformatics · 2020Article
- OutPredict: multiple datasets can improve prediction of expression and inference of causality.Scientific reports · 2020Article
- Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation.Journal of healthcare engineering · 2020Article
- Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling.Plant physiology · 2019Article
- Parametric and non-parametric gradient matching for network inference: a comparison.BMC bioinformatics · 2019Article
- Phenotyping a Dynamic Trait: Leaf Growth of Perennial Ryegrass Under Water Limiting Conditions.Frontiers in plant science · 2019Article
- High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.Frontiers in genetics · 2019Article
- Gene Expression Value Prediction Based on XGBoost Algorithm.Frontiers in genetics · 2019Article
- Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development inNPJ systems biology and applications · 2019Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo datasets. Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems. For larger GRNs, containing hundreds of genes, these non-parametric approaches more accurately infer network structures than do traditional approaches, but at significant computational cost. For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based methods and those using simple ODEs models.
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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.