Shuwei Yao

TL;DR: The third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed, concluded that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not.

TL;DR: It is reported that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and the ability to manage big data in the era of large experimental screens.

TL;DR: NetGO is proposed, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information and significantly outperforms GOLabeler and other competing methods.

TL;DR: NetGO 2.0 as mentioned in this paper incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework, which further improves the performance of large-scale AFP.

TL;DR: DeepGraphGO as mentioned in this paper is an end-to-end, multispecies graph neural network-based method for autoencoder, which makes the most of both protein sequence and high-order protein network information.