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license: apache-2.0 |
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inference: false |
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--- |
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# Enformer |
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Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer). |
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This particular model was trained on sequences of 196,608 basepairs, target length 896, with shift augmentation but without reverse complement, on poisson loss objective. Final human pearson R of ~0.45. |
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This repo contains the weights of the PyTorch implementation by Phil Wang as seen in the [enformer-pytorch repository](https://github.com/lucidrains/enformer-pytorch). |
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Disclaimer: The team releasing Enformer did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence. |
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We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x) published in Nature for details. |
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### How to use |
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Refer to the README of [enformer-pytorch](https://github.com/lucidrains/enformer-pytorch) regarding usage. |
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### Citation info |
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``` |
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Avsec, Ž., Agarwal, V., Visentin, D. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196–1203 (2021). https://doi.org/10.1038/s41592-021-01252-x |
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``` |
