Metadata-Version: 2.4
Name: alphagenome-pytorch
Version: 0.0.9
Summary: AlphaGenome
Project-URL: Homepage, https://pypi.org/project/alphagenome-pytorch/
Project-URL: Repository, https://github.com/lucidrains/alphagenome
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Phil Wang
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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        SOFTWARE.
License-File: LICENSE
Keywords: artificial intelligence,attention mechanism,deep learning,genomics,splicing,transformers
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: einops>=0.8.0
Requires-Dist: einx>=0.3.0
Requires-Dist: torch>=2.4
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

<img src="./extended-figure-1.png" width="450px"></img>

## AlphaGenome (wip)

Implementation of [AlphaGenome](https://deepmind.google/discover/blog/alphagenome-ai-for-better-understanding-the-genome/), Deepmind's updated genomic attention model

## Install

```bash
$ pip install alphagenome-pytorch
```

## Usage

```python
import torch
from alphagenome_pytorch import TransformerTower

transformer = TransformerTower(dim = 768, dim_pairwise = 128)

single = torch.randn(2, 512, 768)

attended_single, attended_pairwise = transformer(single)
```

## Citations

```bibtex
@article{avsec2025alphagenome,
  title   = {AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model},
  author  = {Avsec, {\v{Z}}iga and Latysheva, Natasha and Cheng, Jun and Novati, Guido and Taylor, Kyle R and Ward, Tom and Bycroft, Clare and Nicolaisen, Lauren and Arvaniti, Eirini and Pan, Joshua and Thomas, Raina and Dutordoir, Vincent and Perino, Matteo and De, Soham and Karollus, Alexander and Gayoso, Adam and Sargeant, Toby and Mottram, Anne and Wong, Lai Hong and Drot{\'a}r, Pavol and Kosiorek, Adam and Senior, Andrew and Tanburn, Richard and Applebaum, Taylor and Basu, Souradeep and Hassabis, Demis and Kohli, Pushmeet},
  year    = {2025}
}
```
