license: apache-2.0 inference: false
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 131,072 basepairs, target length 896 on v3-64 TPUs for 3 days with sequence augmentations and pearson correlation objective.
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.
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.
Refer to the README of enformer-pytorch regarding usage.
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