Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction.

ABSTRACT: Accurate identification of protein function is critical to elucidate life mechanisms and design new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology (GO) attributes of proteins through a triplet neural-network architecture embedded with pre-trained language models from protein sequences. The method was systematically tested on 1068 non-redundant benchmarking proteins and 3328 targets from the third Critical Assessment of Protein Function Annotation (CAFA) challenge. Experimental results showed that ATGO achieved a significant increase of the GO prediction accuracy compared to the state-of-the-art approaches in all aspects of molecular function, biological process, and cellular component. Detailed data analyses showed that the major advantage of ATGO lies in t

SUBMITTER: Zhu YH 

PROVIDER: S-EPMC9822105 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature