ArticleProceedings of the National Academy of Sciences of the United States of America2018
Windowed Granger causal inference strategy improves discovery of gene regulatory networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- lagCI enables inference of temporal causal relationships from dense multi-omic time series.Briefings in bioinformatics · 2026Article
- Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.NPJ systems biology and applications · 2026Article
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- Nonlinear dynamic genetic regulation identifies peripheral drivers of neurodegenerative disease progression.medRxiv : the preprint server for health sciences · 2026Article
- Systems acclimation to osmotic stress in zygnematophyte cells.Nature communications · 2026Article
- Time-resolved oxidative signal convergence across the algae-embryophyte divide.Nature communications · 2025Article
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions.Scientific reports · 2024Article
- Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.Entropy (Basel, Switzerland) · 2023Article
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference.PNAS nexus · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Integrating bioinformatic strategies in spatial life science research.Briefings in bioinformatics · 2022Review
- A novel approach GRNTSTE to reconstruct gene regulatory interactions applied to a case study for rat pineal rhythm gene.Scientific reports · 2022Article
- Data-driven learning how oncogenic gene expression locally alters heterocellular networks.Nature communications · 2022Article
- Granger causal inference on DAGs identifies genomic loci regulating transcription.... International Conference on Learning Representations · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes.Frontiers in genetics · 2022Article
- The impact of Traditional Chinese Medicine on mouse gut microbiota abundances and interactions based on Granger causality and pathway analysis.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene-gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene-gene influences.
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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 Socratic graph.