geniml

Topics in the source

• Geniml: Genomic Interval Machine Learning
• Overview
• Installation
• Core Capabilities
• 1. Region2Vec: Genomic Region Embeddings
• 2. BEDspace: Joint Region and Metadata Embeddings
• 3. scEmbed: Single-Cell Chromatin Accessibility Embeddings
• 4. Consensus Peaks: Universe Building

Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks. Install geniml using uv: For ML dependencies (PyTorch, etc.): Development version from GitHub: Geniml provides five primary capabilities, each detailed in dedicated reference files: Train unsupervised embeddings of genomic regions using word2vec-style learning. Use for: Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML. Workflow: 1. Tokenize BED files using a universe reference 2. Train Region2Vec model on tokens 3. Generate embeddings for regions Reference: See references/region2vec.md for detailed workflow, parameters, and examples. Train shared embeddings for region sets and metadata labels using StarSpace. Use for: Metadata-aware searches, cross-modal queries (region→label or label→region), joint analysis of genomic content and experimental conditions. Workflow: 1. Preprocess regions and metadata 2. Train BEDspace model 3. Compute distances 4. Query

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