Geniml
技能 已验证 活跃This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
To enable machine learning tasks on genomic interval data by providing unsupervised methods for learning embeddings and defining consensus regions, facilitating tasks like similarity analysis, clustering, and feature generation for downstream ML.
功能
- Train genomic region embeddings (Region2Vec)
- Learn joint region and metadata embeddings (BEDspace)
- Generate single-cell ATAC-seq embeddings (scEmbed)
- Build consensus peak sets (universes)
- Provide utility tools for tokenization, caching, and randomization
使用场景
- Training region embeddings for similarity analysis or downstream ML
- Analyzing scATAC-seq data for cell clustering and annotation
- Building reference peak sets for standardization
- Querying genomic regions based on associated metadata
非目标
- Directly performing wet-lab experimental design
- Providing a GUI for analysis
- Replacing core bioinformatics tools for raw data processing (e.g., alignment)
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
Geniml
99Part of the AlterLab Academic Skills suite. This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
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