MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.

ABSTRACT: Accurately identifying protein functions is essential to understand life mechanisms and thus advance drug discovery. Although biochemical experiments are the gold standard for determining protein functions, they are often time-consuming and labor-intensive. Here, we proposed a novel composite deep-learning method, Multi-source Knowledge Fusion for Gene Ontology prediction (MKFGO), to infer Gene Ontology (GO) attributes through integrating five complementary pipelines built on multi-source biological data. MKFGO was rigorously benchmarked on 1522 nonredundant proteins, demonstrating superior performance over 12 state-of-the-art function prediction methods. Comprehensive data analyses revealed that the major advantage of MKFGO lies in its two deep-learning components, handcrafted feature rep

SUBMITTER: Zhu YH 

PROVIDER: S-EPMC12354956 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature