Genomic Interval Machine Learning

关于

Geniml is a comprehensive toolkit for building machine learning models on genomic interval data, such as BED files. It offers unsupervised methods for learning low-dimensional embeddings of genomic regions, single cells, and metadata labels, enabling powerful similarity searches, clustering, and downstream analysis. Whether building consensus peak sets, analyzing single-cell chromatin accessibility, or performing cross-modal queries between experimental conditions and genomic features, this skill provides the implementation patterns and workflows necessary for advanced genomic feature learning.

主要功能

• Dimensionality reduction and clustering for single-cell ATAC-seq data
• Generation of unsupervised genomic region embeddings via Region2Vec
• 2,066 GitHub stars
• Joint representation learning for genomic regions and metadata labels
• Genomic data randomization and tokenization utilities
• Statistical methods for building reference peak universes

使用场景

• Searching genomic databases for regions similar to specific experimental conditions
• Preprocessing and tokenizing large-scale BED file collections for machine learning
• Analyzing single-cell ATAC-seq data for cell-type annotation and clustering