Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Justin D. Finkle, Jia J. Wu, Neda Bagheri*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2252-2257
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number9
StatePublished - Feb 27 2018


  • Gene regulatory networks
  • Granger causality
  • Machine learning
  • Network inference
  • Time-series analysis

ASJC Scopus subject areas

  • General


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