AI导哈

数据与研究

arboreto

k-dense-ai/scie…t-skills

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.

  • 1942 次安装
  • 5.5万 星标
  • 收录于

安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill arboreto

SKILL.md

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.

Installation and compatibility

The following exact stack passed dense and CSC-sparse GRNBoost2, dense GENIE3, custom GBM/RF, and wrapper smoke tests on macOS arm64 with Python 3.11.11:

相关 Skills

scientific-writingk-dense-ai/scie…t-skillsDrafts, revises, and audits scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.营销与写作2480scientific-visualizationk-dense-ai/scie…t-skillsCreates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.数据与研究2447scientific-critical-thinkingk-dense-ai/scie…t-skillsEvaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.测试与安全2404literature-reviewk-dense-ai/scie…t-skillsConducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.测试与安全2382scientific-brainstormingk-dense-ai/scie…t-skillsFacilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.智能体工作流2335statistical-analysisk-dense-ai/scie…t-skillsGuided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.数据与研究2318