What type of data does Geniml support?

Geniml primarily works with genomic interval data in BED format, including ChIP-seq and ATAC-seq datasets.

What is a 'Universe' in the context of this skill?

A Universe is a reference set of consensus peaks used to standardize and tokenize genomic regions across multiple datasets.

Can I use Geniml for single-cell analysis?

Yes, the scEmbed capability is specifically designed for single-cell chromatin accessibility embeddings and integration with scanpy workflows.

Does Geniml support joint metadata searches?

Yes, the BEDspace feature allows you to create a shared embedding space for both genomic regions and their associated experimental metadata labels.

Genomic Machine Learning (Geniml)

Name: Genomic Machine Learning (Geniml)
Author: pur3v4d3r

bypur3v4d3r

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数据科学与机器学习

Builds machine learning models and unsupervised embeddings for genomic interval data and BED file collections.

关于

Geniml is a specialized toolkit designed for processing genomic interval data from BED files into machine-learning-ready formats. It enables the creation of unsupervised region embeddings using Region2Vec, joint metadata-region analysis via BEDspace, and single-cell chromatin accessibility studies with scEmbed. By standardizing the way genomic 'universes' are built and tokenized, Geniml allows researchers to perform similarity searches, clustering, and downstream analysis on large-scale chromatin accessibility datasets with statistical rigor.

主要功能

1 GitHub stars
Region2Vec for unsupervised genomic region embeddings
Automated 'Universe' building for consensus peak sets
scEmbed for single-cell ATAC-seq dimensionality reduction
BEDspace for joint embedding of regions and metadata labels
BED file tokenization and evaluation utilities

使用场景

Clustering and cell-type annotation for scATAC-seq data
Building feature vectors for supervised genomic ML models
Similarity searches across large BED file repositories

关于

主要功能

1 GitHub stars
Region2Vec for unsupervised genomic region embeddings
Automated 'Universe' building for consensus peak sets
scEmbed for single-cell ATAC-seq dimensionality reduction
BEDspace for joint embedding of regions and metadata labels
BED file tokenization and evaluation utilities

使用场景

Clustering and cell-type annotation for scATAC-seq data
Building feature vectors for supervised genomic ML models
Similarity searches across large BED file repositories