domains / families / science / arboreto

arboreto

combined

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. 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.

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 i…
• You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges.
• You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions.
• You need GRN inference that can scale to large datasets using parallel/distributed execution.
• You want to compare gradient-boosting–based GRN inference (GRNBoost2) versus random-forest–based inference (GENIE3).
• You need a reproducible, scriptable pipeline to generate a ranked network edge list from expression data.

Skill page

Conflict

The three copies overlap on the core task (GRN inference with GRNBoost2/GENIE3 via Dask), but each contains material steps the others lack. K-Dense-AI provides a pinned, tested compatibility stack, the Dask query-planning workaround, sparse/SciPy constraints, and a bundled wrapper with validation. FreedomIntelligence provides broader usage patterns (single-cell, bulk TF filtering, comparative conditions, pySCENIC integration) and cluster client examples. aipoch provides a different CLI entrypoint and a high-level feature/parameter summary. Crowning one would hide real alternatives and constraints.