geniml
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.
This page mirrors an upstream repository entry. It does not mean the skill is already part of the SCI Skills curated catalog.
- Raw path
- scientific-skills/geniml
- Allowed tools
- -
- Repository version
- 2.31.0
- Synced at
- March 27, 2026
About this skill
About this skill
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.
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