Geniml
Skill Verifiziert AktivThis 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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- Directly performing wet-lab experimental design
- Providing a GUI for analysis
- Replacing core bioinformatics tools for raw data processing (e.g., alignment)
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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