Laiyi Fu

Hi there, The training dataset we used in the publication can be retrieved from mxfold2 paper(RNAStralign and TR0), you can download that data from its [repository](https://github.com/keio-bioinformatics/mxfold2), as for the fine-tuning...

Hi there, As illustrated in our paper, we trained the model on TR0 & RNAStralign & augmented mutated dataset for ufold_train.pt. TS0 is only used for training(the dataset we used...

Hi there, We downloaded these two datasets from e2efold paper, which can be retrieved from here (https://drive.google.com/open?id=19KPRYJjjMJh1qdMhtmUoYA_ncw3ocAHc). Thanks

Hi there, I'm afraid the problem occurs probably because of the package version issue. Could you please check the version of Munch package to see whether it match the software...

Hi, Sorry for the inconvenience, we have rename that model file in the code to ufold_train_alldata.pt, which is included in the google drive. Please check it out. Thanks

Hi Michele, Thanks for reaching out. UFold could go up to 1600bp. But as the sequence gets too long, it will inevitably cost a lot memory usage and time to...

Hi there, You may refer to (https://github.com/uci-cbcl/UFold/blob/5f2be9c1ccb3fc01869dccb5bb8a180a2740c6f9/ufold/data_generator.py#L32) and (https://github.com/uci-cbcl/UFold/blob/5f2be9c1ccb3fc01869dccb5bb8a180a2740c6f9/ufold/data_generator.py#L47) to change this to single core, but be aware that it will result in lower running time.

Hello, To ensure the successful deployment of the UFold environment, it's crucial that your base environment matches the specifications preset in the YAML file. As of our latest update, we...

Hi there, Sorry for the inconvenience, we are now undergoing some hardware issues, and we are upgrading our backend server recently, please wait for a couple of days. Thanks, Laiyi

Sorry for the inconvenience, the hardware issues bugs us as well recently, we are maintaining of backend webserver, and plan to set it to normal in a few days. We...