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--- |
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language: |
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- en |
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tags: |
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- biology |
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- genomics |
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- codon-optimization |
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- p-adic-math |
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- hyperbolic-geometry |
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- ddg-prediction |
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license: other |
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metrics: |
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- spearmanr |
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--- |
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# Ternary Codon Encoder: P-adic Hyperbolic Embeddings |
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The Ternary Codon Encoder is a neural embedding model that maps the 64 genetic codons into a 16-dimensional hyperbolic space. It is the first model to explicitly use **3-adic valuation** as a mathematical prior to organize the genetic code's hierarchical structure. |
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## Model Description |
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- **Architecture:** MLP-based encoder (12-dim one-hot input $
ightarrow$ 16-dim hyperbolic output). |
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- **Mathematical Foundation:** Leverages 3-adic mathematics to represent the discrete hierarchy of the codon table. |
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- **Latent Space:** Poincaré ball where radial distance encodes 3-adic valuation (conservation/variability). |
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## Key Discoveries |
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- **Physics Dimension:** Latent dimension 13 correlates strongly ($
ho = -0.70$) with molecular mass, volume, and force constants ($k$). |
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- **Linear Stability Manifold:** Provides high-quality feature vectors for sequence-only protein stability ($\Delta\Delta G$) prediction. |
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- **Synonymous Cohesion:** Synonymous codons cluster together in hyperbolic space while maintaining clear boundaries between amino acid groups. |
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## Performance |
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- **DDG Spearman $
ho$:** 0.614 (Sequence-only benchmarking on diverse datasets). |
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- **Improvement:** +105% over baseline p-adic embedding models. |
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## Usage |
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```python |
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import torch |
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from trainable_codon_encoder import TrainableCodonEncoder |
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# Load model |
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encoder = TrainableCodonEncoder(latent_dim=16, hidden_dim=64) |
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checkpoint = torch.load("pytorch_model.bin", map_location="cpu") |
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encoder.load_state_dict(checkpoint["model_state_dict"]) |
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encoder.eval() |
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# Get embedding for a codon (e.g., ATG index 14) |
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codon_idx = torch.tensor([14]) |
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with torch.no_grad(): |
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z_hyp = encoder(codon_idx) |
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print(f"Hyperbolic Embedding: {z_hyp}") |
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``` |
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## Citation |
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```bibtex |
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@software{ternary_codon_2026, |
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author = {AI Whisperers}, |
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title = {Ternary Codon Encoder: P-adic Hyperbolic Embeddings}, |
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year = {2026}, |
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url = {https://huggingface.co/ai-whisperers/ternary-codon-encoder} |
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} |
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``` |
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