Train Region2Vec embeddings on genomic interval data from BED files.
npx skills add https://github.com/Sologa/codex-pipeline --skill geniml-sologa
Please help me install this Agent Skill. Skill: geniml Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/geniml Command: npx skills add https://github.com/Sologa/codex-pipeline --skill geniml-sologa
This Skill simplifies complex machine learning tasks on genomic interval data (like BED files), enabling advanced analysis of DNA regions and single-cell data without deep ML expertise.
Use the geniml skill to train Region2Vec embeddings on BED files in the 'bed_files/' directory, saving the model to 'model/'.
High-intent search queries and answers about installing and using this skill.
You can train Region2Vec embeddings on BED files to learn vector representations of genomic regions, enabling similarity analysis and clustering of DNA intervals without requiring deep machine learning expertise.
Yes, the scEmbed feature generates single-cell embeddings from scATAC-seq data, allowing you to identify distinct cell populations by clustering them using scanpy for downstream analysis.
BEDspace jointly embeds genomic regions and their associated metadata, such as cell types, to enable powerful cross-modal searches and integrated analysis of interval data with experimental annotations.
You can build standardized consensus peak universes from multiple experiments to establish a consistent reference set of genomic intervals for uniform downstream analysis and evaluation.
Yes, training region embeddings requires Python libraries including PyTorch and StarSpace, along with scanpy, to process BED files and scATAC-seq data for machine learning tasks.
Machine learning on genomic interval data captures complex patterns in DNA regions and single-cell profiles that standard statistical methods miss, transforming raw BED files into quantitative vector representations for deeper analysis.
sanand0Train Region2Vec embeddings on genomic interval BED files.
StarlitnightlyAutomate single-cell RNA-seq preprocessing with omicverse notebooks for QC, HVG detection, and embeddings.
felixboehmTrain Region2Vec embeddings on genomic interval BED files.
jackspaceGenerate unsupervised embeddings for genomic intervals from BED files.
