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geniml

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Machine learning toolkit for genomic interval (BED) data; use it when you need to tokenize BED collections and train embeddings for regions/cells/labels, build consensus peak universes, or run similarity search and downstream ML on chromatin accessibility datasets.

scientific-skills/Data Analysis/geniml/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

- **You have many BED files and need numeric features** for clustering, similarity search, or downstream supervised learning (e.g., ChIP-seq/ATAC-seq region sets).
- **You want unsupervised embeddings of genomic regions** to compare region sets across experiments (Region2Vec).
- **You need joint embeddings of regions and metadata labels** (e.g., tissue/cell type/condition) to enable cross-modal queries like *Region → Label* or *Label → Region* (BEDspace).
- **You are analyzing single-cell ATAC-seq** and want cell embeddings for clustering/annotation and integration with Scanpy workflows (scEmbed).
- **You need a consensus peak set (“universe”)** built from multiple BED files to standardize tokenization and region definitions across datasets (Universe construction).

## Key Features

- **Region2Vec**: Word2vec-style unsupervised embeddings for genomic regions from tokenized BED data.
- **BEDspace**: StarSpace-based joint embedding space for region sets and metadata labels; supports similarity search and cross-modal retrieval.
- **scEmbed**: Single-cell ATAC-seq embedding workflow (tokenize cells → train → encode cells) compatible with Scanpy.
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