Northwestern University researchers say they have developed a new machine-learning algorithm that can help scientists better understand the genetic interactions inside cellular networks. Called "Sliding Window Inference for Network Generation," or SWING, the algorithm uses time-series data to reveal the underlying structure of cellular networks.
The research (“Windowed Granger causal inference strategy improves discovery of gene regulatory networks”) is published in PNAS .
“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,” write the investigators.
“We address this limitation by introducing 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 ...