ArticleBioinformatics (Oxford, England)2024
InterLabelGO+: unraveling label correlations in protein function prediction.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- StoPred: Accurate Stoichiometry Prediction for Protein Complexes Using Protein Language Models and Graph Attention.Research square · 2025Article
- StoPred: Accurate Stoichiometry Prediction for Protein Complexes Using Protein Language Models and Graph Attention.bioRxiv : the preprint server for biology · 2025Article
- Conserved Heterochromatin-like Structures with Local Regulators Mediate the Iron Stress Response in Mycobacteria.bioRxiv : the preprint server for biology · 2025Article
- EZpred: improving deep learning-based enzyme function prediction using unlabeled sequence homologs.bioRxiv : the preprint server for biology · 2025Article
- ProtGO: universal protein function prediction utilizing multi-modal gene ontology knowledge.Bioinformatics (Oxford, England) · 2025Article
- MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation.Bioinformatics (Oxford, England) · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
motivationAccurate protein function prediction is crucial for understanding biological processes and advancing biomedical research. However, the rapid growth of protein sequences far outpaces the experimental characterization of their functions, necessitating the development of automated computational methods.
resultsWe present InterLabelGO+, a hybrid approach that integrates a deep learning-based method with an alignment-based method for improved protein function prediction. InterLabelGO+ incorporates a novel loss function that addresses label dependency and imbalance and further enhances performance through dynamic weighting of the alignment-based component. A preliminary version of InterLabelGO+ achieved a strong performance in the CAFA5 challenge, ranking sixth out of 1625 participating teams. Comprehensive evaluations on large-scale protein function prediction tasks demonstrate InterLabelGO+'s ability to accurately predict Gene Ontology terms across various functional categories and evaluation metrics. AVAILABILITY AND IMPLEMENTATION: The source code and datasets for InterLabelGO+ are freely available on GitHub at https://github.com/QuanEvans/InterLabelGO. A web-server is available at https://seq2fun.dcmb.med.umich.edu/InterLabelGO/. The software is implemented in Python and PyTorch, and is supported on Linux and macOS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.