README for DeepFormer DeepFormer is a hybrid network based on convolutional neural network and linear attention mechanism for identifying the function of DNA sequence.We compared DeepFormer with five excellent existing models on our own platform. Although the replication results differ from the original reported results of the five models, DeepFormer achieves an advanced performance of 0.9504AV_AUROC, 0.4658AV_AUPR, which is far superior to other prediction methods, both in terms of replication results and original paper results. The performance results of all models described in the original paper are shown in the following table. DeepSEA DanQ DanQ_JASPAR DeepATT DeepFormer AV_AUROC 0.93260 0.93837 0.94174 0.94519 0.9504 AV_AUPR 0.34163 0.37089 0.37936 0.39522 0.4658
Requirement Python (3.9) | selene (0.4.4.0) | einops (0.5.0) | pytorch (1.7.1) | torchvision (0.8.2) | torchaudio (0.7.2)
Data You need to first download the training, validation, and testing sets from DeepSEA. You can download the datasets from http://deepsea.princeton.edu/media/code/deepsea_train_bundle.v0.9.tar.gz After you have extracted the contents of the tar.gz file, move the 3 .mat files into the data/ folder. We also transform the .mat files to .fasta files, you can get them from there https://pan.baidu.com/s/1AaMBCgTzysljGg_JhyUKfA, and the extraction code is a723. Similarly, move the files into the data/ folder. The maize dataset used in the model generalization capability evaluation is also at the above link.
Usage If you have everything installed, you can train a model initially as follows sbatch DeepFormer_train.slurm
We have saved the optimal model at the same link above, you can get it from there.
If you do not want to train a model from scratch and just want to do predictions, please download the optimal model, then sbatch predict.slurm
Acknowledgement Thanks for Selene, a PyTorch-based deep learning library for sequence data, makes contribution to our research.