Infer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
npx skills add https://github.com/sanand0/scientific-research --skill arboreto-sanand0
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/arboreto Command: npx skills add https://github.com/sanand0/scientific-research --skill arboreto-sanand0
This Skill automates the complex process of inferring gene regulatory networks (GRNs) from gene expression data, helping researchers understand gene interactions and biological pathways.
Run basic GRN inference using the script with your expression data and desired output file.
High-intent search queries and answers about installing and using this skill.
You infer gene regulatory networks from single-cell RNA-seq data by applying scalable algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target relationships and regulatory interactions.
The best way to scale gene regulatory network inference for large transcriptomics datasets is to use distributed computation across multi-core machines and clusters, which this Skill supports via dask.
Yes, you can use GRNBoost2 and GENIE3 for bulk RNA-seq gene expression analysis, as the Skill supports both bulk and single-cell transcriptomics data to map regulatory interactions.
You do not strictly need dask and distributed computing for small datasets, but these dependencies enable scalable inference across multi-core machines and clusters for massive transcriptomics datasets.
GRNBoost2 and GENIE3 are the available algorithms used to identify transcription factor-target relationships and regulatory interactions from gene expression data.
tassiovaleInfer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
jimmc414Infer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.
Lord1EgyptInfer gene regulatory networks from expression data using gradient boosting and random forests.
Hung-3008Infer gene regulatory networks from expression data with GRNBoost2 or GENIE3.
