Train genomic interval embeddings from BED data for similarity search.
npx skills add https://github.com/mkurman/tamux --skill geniml-mkurman
Please help me install this Agent Skill. Skill: geniml Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/geniml Command: npx skills add https://github.com/mkurman/tamux --skill geniml-mkurman
Geniml provides a unified framework to build and analyze machine learning models on genomic interval data using BED files. It enables tokenization, region embeddings, and joint region-metadata representations to power similarity search, clustering, and downstream analyses.
Train a Region2Vec model on your BED dataset by tokenizing with a universe and then evaluate the embeddings.
High-intent search queries and answers about installing and using this skill.
To generate genomic interval embeddings from BED files, you tokenize your data using a built universe and train a Region2Vec model to produce unsupervised representations for similarity search and clustering.
Region2Vec is used for unsupervised embeddings of genomic regions, enabling dimensionality reduction and similarity search across bulk BED collections to help identify patterns in interval data.
Yes, you can use the scEmbed feature for single-cell ATAC-seq data. It generates embeddings integrated with scanpy workflows to support scalable clustering and cross-dataset benchmarking.
To tokenize BED datasets for machine learning preprocessing, you build consensus peaks to create a universe, which standardizes your intervals into references for model training and evaluation.
Yes, the BEDspace feature creates joint embeddings of genomic regions and metadata labels, allowing you to perform cross-modal queries that link interval data with biological annotations.
Cross-dataset benchmarking of bulk BED collections requires standardized universe building and tokenization checks. Without consensus peaks, interval mismatches across datasets may limit reproducibility and evaluation accuracy.
shushuznBuild machine learning embeddings from genomic interval BED files.
hxk622Train genomic region embeddings and analyze single-cell ATAC-seq data with Python.
RamanEbrahimiPerform genomic interval operations and bioinformatics file I/O on Polars DataFrames.
jackspaceGenerate unsupervised embeddings for genomic intervals from BED files.
