NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.

ABSTRACT: With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification. Under the learning to rank (LTR) framework, our previous NetGO tool integrated massive networks and multi-type information about protein sequences to achieve good performance by dealing with all possible GO terms (>44 000). In this work, we propose the updated version as NetGO 2.0, which further improves the performance of large-scale AFP. NetGO 2.0 also incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework. We generate dat

SUBMITTER: Yao S 

PROVIDER: S-EPMC8262706 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature