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Efficient computational methods for protein functional annotation help bridge the gap between high-throughput sequence data and unknown protein functions. While many data-driven methods predict protein functions based on protein-level information, they often overlook the relationships between different functions. In this work, we introduce PFresGO, an attention-based deep learning approach that utilizes the hierarchical structure of gene ontology (GO) graphs to predict multiple protein functions in a high-throughput manner. PFresGO is available for academic use at https://github.com/BioColLab/PFresGO . We provide an overview of our predictor, discuss its ability to accurately predict protein functions, and demonstrate how to interpret the results.

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Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO

  • 实验方案

  • 发表日期：

    2025年

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  • Tong Pan,
  • Geoffrey I. Webb,
  • Seiya Imoto,
  • Jiangning Song

摘要

Efficient computational methods for protein functional annotation help bridge the gap between high-throughput sequence data and unknown protein functions. While many data-driven methods predict protein functions based on protein-level information, they often overlook the relationships between different functions. In this work, we introduce PFresGO, an attention-based deep learning approach that utilizes the hierarchical structure of gene ontology (GO) graphs to predict multiple protein functions in a high-throughput manner. PFresGO is available for academic use at https://github.com/BioColLab/PFresGO . We provide an overview of our predictor, discuss its ability to accurately predict protein functions, and demonstrate how to interpret the results.