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About arboreto
arboreto
Infers candidate gene regulatory networks
arboreto is an agent skill for inferring candidate gene regulatory networks from bulk or single-cell expression data, using the Arboreto library's GRNBoost2 and GENIE3 methods. GRNBoost2 uses stochastic gradient boosting regressions, and GENIE3 uses random forests. Each target gene is predicted from candidate regulators, excluding itself, to rank regulator-target associations. The skill is clear about what these results mean: they are observational predictive associations, not proof of direct binding, activation, repression or causal regulation. It also warns about the library's state. The latest PyPI release it checked was 0.1.6 from February 2021, and the Read the Docs site labels its docs 0.1.5, while the current GitHub source contains fixes that are not in the PyPI wheel. So an unpinned installation may not run inference, and the skill lists an exact stack that passed its smoke tests on macOS arm64 with Python 3.11. A reference file covers distributed computing. Use it when you need candidate regulators for a set of genes from expression data. It is part of the k-dense-ai scientific agent skills repository on GitHub, and the listing shows it as free.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/arboreto