Ibtehaz, N., Kagaya, Y. & Kihara, D. Domain-PFP allows protein function prediction using function-aware domain embedding representations. Below are the data used in this work. Data required to reproduce our experiments. Please refer to Experiments for more information.

You can use Domain-PFP to compute functionally aware embedding representation of a protein by providing the protein ID or path to a fasta file. You also need to provide the path to the savefile, where the embedding will be saved as a pickle file

2023年10月31日 · Here, we introduce Domain-PFP, a protein function prediction method that uses functional representation of proteins through domain-GO association learned by a self-supervised method from...

2023年8月24日 · Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, significantly outperformed the state-of-the-art function predictors.

此外，基于域嵌入的新函数预测方法（称为 Domain-PFP）大大优于最先进的函数预测器。 此外，Domain-PFP在CAFA3评估中表现出了竞争性的表现，在参与评估的顶级团队中取得了总体最佳表现。

2023年8月24日 · Here, we introduce Domain-PFP, a protein function prediction method that uses functional representation of proteins through domain-GO association learned by a self-supervised method from protein databases.

2023年8月23日 · Here, we introduce Domain-PFP, a protein function prediction method that uses functional representation of proteins through domain-GO association learned by a self-supervised method from protein databases.

2023年10月31日 · Here, we introduce Domain-PFP, a protein function prediction method that uses functional representation of proteins through domain-GO association learned by a self-supervised method from

Domain-PFP is a self-supervised method to predict protein functions from the domains - kiharalab/Domain-PFP

Here, we introduce Domain-PFP, a protein function prediction method that uses functional representation of proteins through domain-GO association learned by a self-supervised method from protein databases.