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geniml
Maintainer K-Dense Inc. · Last updated April 1, 2026
Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.
Original source
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/geniml
- Maintainer
- K-Dense Inc.
- License
- BSD-2-Clause license
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.
- hard_tokenization( src_folder='bed_files/', dst_folder='tokens/', universe_file='universe.bed', p_value_threshold=1e-9 ).
Source Doc
Excerpt From SKILL.md
Installation
Install geniml using uv:
For ML dependencies (PyTorch, etc.):
Development version from GitHub:
Core Capabilities
Geniml provides five primary capabilities, each detailed in dedicated reference files:
1. Region2Vec: Genomic Region Embeddings
Train unsupervised embeddings of genomic regions using word2vec-style learning.
Use for: Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.
Workflow:
- Tokenize BED files using a universe reference
- Train Region2Vec model on tokens
- Generate embeddings for regions
Reference: See references/region2vec.md for detailed workflow, parameters, and examples.
Use cases
- 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.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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