bio-temporal-genomics-temporal-grn
Infers dynamic gene regulatory networks from bulk time-series expression data using Granger causality (statsmodels), dynGENIE3 (Extra-Trees on ODE-derived expression derivatives), and dynamic Bayesian networks (bnlearn). Identifies time-delayed regulatory relationships and tracks network rewiring across conditions. Use when inferring causal regulatory relationships from bulk temporal expression data or detecting TF influence propagation over time. Not for static co-expression networks (see gene-regulatory-networks/coexpression-networks).
npx skills add BioTender-max/awesome-bio-agent-skills --skill temporal-grn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: numpy 1.26+, pandas 2.2+, statsmodels 0.14+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Temporal Gene Regulatory Network Inference **"Infer causal regulatory relationships from my time-series expression data"** → Identify time-delayed TF-target regulatory edges from bulk temporal expression using Granger causality testing, dynGENIE3 tree-based ODE inference, or dynamic Bayesian networks. - Python: `statsmodels.tsa.stattools.grangercausalitytests()` for Granger causality - R: `dynGENIE3::dynGENIE3()` for ODE-based GRN inference from time series Infers directed, time-delayed regulatory relationships from bulk time-series expression data. Captures how transcription factor activity propagates through gene regulatory networks over time. ## Core Workflow 1. Select candidate regulators (
- Version Compatibility
- Core Workflow
- Granger Causality (Python/statsmodels)
- Pairwise Granger Test
- Multiple Testing Correction
- Build Adjacency Matrix
- Stationarity Check
- dynGENIE3 (R)
- Basic dynGENIE3
- With Known Regulators
- Multiple Time Series
- Dynamic Bayesian Networks (R/bnlearn)
- Structure Learning
- Bootstrap for Edge Confidence
What does the bio-temporal-genomics-temporal-grn skill do?
Infers dynamic gene regulatory networks from bulk time-series expression data using Granger causality (statsmodels), dynGENIE3 (Extra-Trees on ODE-derived expression derivatives), and dynamic Bayesian networks (bnlearn). Identifies time-delayed regulatory relationships and tracks network rewiring across conditions. Use when inferring causal regulatory relationships from bulk temporal expression data or detecting TF influence propagation over time. Not for static co-expression networks (see gene-regulatory-networks/coexpression-networks).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill temporal-grn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.