enformer-191k_corr_coef_obj

Enformer

Enformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository.

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 ~0.49.

This repo contains the weights of the PyTorch implementation by Phil Wang as seen in the enformer-pytorch repository.

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.

Model description

Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.

We refer to the paper published in Nature for details.

How to use

Refer to the README of enformer-pytorch regarding usage.

Citation info

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