Train machine learning models on genomic interval BED files with geniml.
npx skills add https://github.com/tassiovale/claude-code-kit --skill geniml-tassiovale
Please help me install this Agent Skill. Skill: geniml Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/geniml Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill geniml-tassiovale
This skill addresses the challenge of applying machine learning to genomic interval data (BED files), enabling users to perform various genomic analyses and tasks efficiently.
Train an embedding model for scATAC-seq data using the scEmbed tool from the geniml skill.
High-intent search queries and answers about installing and using this skill.
To apply machine learning to BED files for genomic interval analysis, you can use tools that build consensus peak sets from BED collections, tokenize the intervals, and train models to generate embeddings for downstream analysis.
Yes, you can generate cell embeddings from single-cell ATAC-seq data using the scEmbed tool, which trains embedding models on genomic intervals and integrates the resulting cell representations with the scanpy analysis framework.
Python dependencies required for genomic machine learning workflows include geniml for the core tools, PyTorch for model training, and scikit-learn for dimensionality reduction and analysis tasks.
You can reduce dimensionality in genomic interval datasets by training embedding models on consensus peak sets to learn lower-dimensional representations of genomic regions and single cells for easier downstream analysis.
Yes, geniml integrates with scanpy by generating cell embeddings from single-cell ATAC-seq data, which can then be used directly within the scanpy framework for clustering, annotation, and further analysis.
A universe in genomic interval machine learning is a consensus peak set built from a collection of BED files, which serves as the vocabulary for tokenizing genomic regions before training embedding models.
yf8578Train Region2Vec embeddings on genomic interval BED files.
StarlitnightlyAutomate single-cell RNA-seq preprocessing with omicverse notebooks for QC, HVG detection, and embeddings.
CompSci-SquadProvides guidance for machine learning with scikit-learn including classification, regression, clustering, preprocessing, evaluation, and tuning.
RamanEbrahimiProcess genomic interval BED files for machine learning tasks.
