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geniml
维护者 K-Dense Inc. · 最近更新 2026年4月1日
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.
原始来源
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/geniml
- 维护者
- K-Dense Inc.
- 许可
- BSD-2-Clause license
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Geniml是一个Python package ，用于 building 机器学习 models on genomic interval data ，面向 BED files. It provides unsupervised methods ，用于 learning embeddings of genomic regions，single cells，、 metadata labels，enabling 相似性检索es，聚类，、 downstream ML tasks。
- hard_tokenization( src_folder='bed_files/'，dst_folder='tokens/'，universe_file='universe.bed'，p_value_threshold=1e-9 )。
原始文档
SKILL.md 摘录
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.
适用场景
- 可用于training region embeddings (Region2Vec，BEDspace)，single-cell ATAC-seq analysis (scEmbed)，building consensus peaks (universes)，或 any ML-based analysis of genomic regions。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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