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geniml

Preprocess genomic interval data and build Region2Vec embeddings for machine learning.

Updated May 8, 2026
Search Tags:#machine learning#scATAC-seq#genomic analysis#genomic embeddings#genomic interval
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill geniml-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geniml
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/bioinformatics-genomics/geniml
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill geniml-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires geniml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines genomic interval machine learning tasks, from data preparation to model training and evaluation, providing a unified toolkit for genomic data analysis.

Core Features & Use Cases

  • Data Preparation: Preprocess genomic interval data for machine learning models.
  • Machine Learning Models: Build models like Region2Vec, BEDspace, and scEmbed for genomic region embeddings and analysis.
  • Model Evaluation: Assess the quality of embeddings using metrics like silhouette scores.
  • Use Case: With a large collection of ChIP-seq data, use this Skill to build embeddings, cluster the data, and annotate cell types from single-cell ATAC-seq experiments.

Quick Start

Run the following command to generate embeddings for your genomic regions using Region2Vec:

uv pip install geniml

Frequently Asked Questions about geniml

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate embeddings for genomic regions from scATAC-seq data?▼

You can generate embeddings for genomic regions by preprocessing scATAC-seq interval data and applying models like Region2Vec or scEmbed to produce vector representations for downstream analysis.

What is genomic interval machine learning used for?▼

Genomic interval machine learning is used for unsupervised learning tasks like similarity search, clustering, and single-cell data integration to annotate cell types from ChIP-seq or ATAC-seq experiments.

Can I use PyTorch for deep learning tasks with genomic interval data?▼

Yes, PyTorch is required for deep learning tasks involving genomic interval data, as it supports building embedding models like Region2Vec, BEDspace, and scEmbed.

How do I evaluate the quality of genomic region embeddings?▼

Evaluate the quality of genomic region embeddings using silhouette scores to measure clustering performance and the separation of similar genomic intervals in the vector space.

What is the best way to cluster large collections of ChIP-seq data?▼

To cluster large ChIP-seq collections, build genomic region embeddings using models like Region2Vec, then apply unsupervised clustering to group similar intervals and annotate cell types from single-cell ATAC-seq data.

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