[2512.05245] STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings

Quantitative Biology > Biomolecules arXiv:2512.05245 (q-bio) [Submitted on 4 Dec 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings Authors:Mehmet Efe Akça, Gökçe Uludoğan, Arzucan Özgür, İnci M. Baytaş View a PDF of the paper titled STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings, by Mehmet Efe Ak\c{c}a and G\"ok\c{c}e Uludo\u{g}an and Arzucan \"Ozg\"ur and \.Inci M. Bayta\c{s} View PDF HTML (experimental) Abstract:Accurate prediction of protein function is essential for elucidating molecular mechanisms and advancing biological and therapeutic discovery. Yet experimental annotation lags far behind the rapid growth of protein sequence data. Computational approaches address this gap by associating proteins with Gene Ontology (GO) terms, which encode functional knowledge through hierarchical relations and textual definitions. However, existing models often emphasize one modality over the other, limiting their ability to generalize, particularly to unseen or newly introduced GO terms that frequently arise as the ontology evolves, and making the previously trained models outdated. We present STAR-GO, a Transformer-based framework that jointly models the semantic and structural characteristics of GO terms to enhance zero-shot protein function prediction. STAR-GO integr...