bio-temporal-genomics-temporal-grn - Dynamic Gene Regulatory Network Inference
Infers time-delayed regulatory relationships from bulk time-series expression data using Granger causality, dynGENIE3, and dynamic Bayesian networks
Tags
Updated: 2026-06-30Capabilities
Typical Inputs
Typical Outputs
What this skill does
- apply Granger causality tests
- run dynGENIE3 inference
- learn dynamic Bayesian networks
- filter regulatory edges
- compare networks across conditions
Inputs
- time-series expression data
- transcription factor list
- time points vector
- candidate regulators
Outputs
- regulatory edge list
- network adjacency matrix
- edge confidence scores
- network comparison metrics
Requirements
- Python 3.x with statsmodels
- R with bnlearn
- R with dynGENIE3
- numpy 1.26+
- pandas 2.2+
- statsmodels 0.14+