Deep Learning for Genomics: Building Multi-Species Foundation Models

The intersection of deep learning and genomics is one of the most exciting frontiers in AI research. ## Why Genomics Needs AI Genomic sequences are incredibly long — the human genome alone is 3 billion base pairs. Traditional alignment-based methods don't scale. Deep learning offers a path to: - Learn evolutionary patterns across species - Predict functional effects of mutations - Discover regulatory elements - Understand gene interactions ## Architecture Choices For genomic foundation models, the architecture choice is critical: - **Transformers**: Quadratic complexity limits sequence length - **Convolutional**: Good for local patterns but limited long-range - **State-Space Models (Mamba SSM)**: Linear complexity, excellent for long sequences ## Our Approach The genomic foundation model I'm building uses Mamba SSM with ~100M parameters, trained on 50+ species. The key innovations: 1. Multi-species tokenization 2. Selective state spaces for evolutionary pattern capture 3. Efficient training with sequence parallelism