ArticlePLoS computational biology2021
Causal network inference from gene transcriptional time-series response to glucocorticoids.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.Briefings in bioinformatics · 2026Article
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- NIPS: Network Inference with Partial State measurements using forced-delay embedding.PNAS nexus · 2026Article
- Unlocking gene regulatory networks for crop resilience and sustainable agriculture.Nature biotechnology · 2025Review
- Time-resolved oxidative signal convergence across the algae-embryophyte divide.Nature communications · 2025Article
- Gaussian processes for time series with lead-lag effects with applications to biology data.Biometrics · 2025Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network.Scientific reports · 2024Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network.Frontiers in molecular biosciences · 2023Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- Inferring causal gene regulatory networkFrontiers in bioengineering and biotechnology · 2022Article
- Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes.Frontiers in genetics · 2022Article
- Review
- Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.Frontiers in bioinformatics · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.
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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.