Individual skill
arboreto
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
skills/arboreto/SKILL.md
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# Arboreto
## When to use
Use Arboreto to rank candidate regulator-target associations from expression
measurements. GRNBoost2 fits stochastic gradient boosting regressions; GENIE3
fits random forests. Each target is predicted from candidate regulators, excluding
itself. These are observational predictive associations, not proof of direct
binding, activation/repression, or causal regulation.
The latest PyPI release checked is **0.1.6** (2021-02-09). Read the Docs still
labels its documentation 0.1.5; the current GitHub source contains fixes that are
**not in the PyPI wheel**. Do not assume a successful unpinned installation can
run inference. See [compatibility and distribution details](references/distributed_computing.md).