lagCI enables inference of temporal causal relationships from dense multi-omic time series.

lagCI accurately infers temporal causal relationships from dense multi-omic time series, identifying over 157,000 interactions in human data.

• Why it matters: Understanding dynamic biological regulation requires reliable methods to uncover causal links in complex, high-dimensional datasets, which current approaches struggle to do effectively.
• What they did: lagCI combines comprehensive lag-correlation profiling with statistical filtering to analyze entire correlation profiles, tested on physiological and multi-omics data, revealing biologically relevant networks.
• The result: The framework successfully recapitulates known biological interactions and uncovers potential molecular hubs, enabling deeper insights into temporal biological processes and supporting broader research applications.